Extraction of Remote Sensing Alteration Information Based on Integrated Spectral Mixture Analysis and Fractal Analysis

Abstract

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area.

1. Introduction

Hydrothermal alteration of wall rocks near ore bodies is primarily the result of interactions between various types of hydrothermal fluids and surrounding rocks, representing the imprints of the progressive enrichment and concentration of ore-forming materials. Quantitative information related to geological structures, ore-bearing geological bodies, and alteration associated with metal mineralization can be comprehensively captured and interpreted through remote sensing-derived alteration signals across various data sources. These signals serve as indicators of potentially altered rocks proximal to mineralization zones, and the alteration information extracted via remote sensing provides a geological basis for guiding mineral deposit prediction [1,2,3,4,5]. As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting [6,7,8]. Although the region’s harsh natural environment imposes multiple technical challenges on conventional surface exploration methods, its sparse vegetation cover offers a distinct advantage for accurately identifying and extracting alteration anomalies using remote sensing techniques [9,10,11]. Therefore, developing a high-precision remote sensing model for alteration information extraction has become urgent in advancing Tibet’s new phase of mineral prospecting studies.

At present, methods such as band ratio analysis [12,13,14], principal component analysis (PCA) [15,16,17], spectral mixture analysis (SMA) [18,19,20], spectral angle mapper (SAM) [21,22,23], and minimum noise fraction (MNF) transformation [24,25,26] are widely applied in the extraction of mineralization-related alteration information. Among these methods, SMA has gradually attracted increasing attention from researchers due to its high accuracy, intuitive interpretability, and robustness. In remote sensing imagery, pixels represent the fundamental units of an image. However, due to spatial resolution limitations and small-scale surface features, a single pixel often contains spectral information from two or more distinct land cover types. Such pixels are referred to as mixed pixels [27,28,29]. The spectral characteristics of remote sensing imagery represent a mixed response of the spectral signatures of various endmembers comprising each pixel. During the extraction of alteration information from remote sensing images, the widespread presence of mixed pixels significantly reduces the accuracy and reliability of land cover identification. Interfering surface features such as vegetation, water bodies, shadows, and fluvial sediments can severely affect the effectiveness of alteration anomaly detection. Therefore, to accurately identify the spectral signatures of useful endmembers, it is essential to perform spectral mixture analysis on the imagery. This process helps to minimize or eliminate the influence of interfering land cover types, improve spectral discrimination accuracy, and ultimately enhance the extraction of alteration information [30].

Complex and irregular phenomena in nature often follow inherent scale-invariant laws. Based on this principle, Mandelbrot introduced the concept of fractal theory in 1983 [31]. As an effective method for quantitatively describing irregular shapes and spatial patterns, fractal theory has been widely applied to studying natural phenomena and complex systems [32,33,34]. This theory enables the extraction of deterministic and regular parameters from seemingly chaotic patterns, allowing the reconstruction of formation mechanisms of fractal structures and the inference of system evolution from stochastic processes. Many geological phenomena and spatial distributions in Earth systems exhibit pronounced fractal characteristics, particularly those associated with hydrothermal mineralization processes such as magma generation, migration, and intrusion, which often display varying degrees of self-similarity. On this basis, fractal theory serves as a powerful tool for identifying and extracting remote sensing-based alteration anomalies, thereby revealing the spatial organization and evolutionary patterns of mineralization. In recent years, fractal-based methods have been widely employed by researchers worldwide for remote sensing anomaly extraction and mineral exploration [35,36,37,38,39].

The Saga region of Tibet is situated in a key segment of central-western India–Eurasia continental collision zone. It spans three major tectonic units from north to south: the Gangdese forearc basin, the Yarlung Zangbo suture zone, and the North Himalaya Tethyan fold–thrust belt. Although the area is characterized by a complex lithostratigraphic framework shaped by multiple phases of superimposed tectonic-magmatic activity, its high-altitude terrain, rugged topography, and limited accessibility pose significant challenges to field-based geological investigations. Against this backdrop, how to efficiently identify alteration anomalies associated with mineralization through remote sensing techniques under conditions where fieldwork is constrained has become a critical scientific question in current mineral exploration efforts in the Saga region. In this context, the present study focuses on the Saga region of Tibet. It employs Level-2A Sentinel-2 imagery to extract remote sensing alteration anomalies, including iron-staining, Al–OH, Mg–OH, and carbonate anomalies by the method of “resampling + mixed pixel decomposition + principal component analysis + mixing ratio + threshold segmentation” in this region. Furthermore, the fractal dimensions of the various alteration anomalies were calculated using fractal theory, enabling the characterization of their spatial distribution patterns and fractal structural features across the study area. Two potential areas have been identified using the composite contour map of fractal dimensions of alteration anomalies obtained from the superimposed analysis of various remotely sensed alteration anomalies. The remote sensing extraction and spatial structure analysis approach developed in this study provides not only a technical solution for mineral prediction in high-altitude and logistically inaccessible regions but also offers theoretical support for the quantitative modeling of alteration anomalies in structurally complex areas. This methodology holds significant theoretical and practical value for mineral exploration in challenging terrains.

2. Overview of the Study Area

2.1. Geological Profile

The study area is characterized by a diverse and extensively distributed set of stratigraphic units, mainly comprising Paleozoic, Mesozoic, and Cenozoic formations. The Paleozoic strata are primarily exposed in the northern part of the region, whereas the Mesozoic and Cenozoic units are widely distributed across the entire area. The Paleozoic sequence includes the Silurian–Ordovician Dajiling Formation, dominated by quartz schist and quartz sandstone, with a mineral assemblage of quartz, feldspar, and minor mica, and characterized by sericitization, malachite alteration and limonitization (

Table 1. Correspondence table of major minerals and alteration characteristics of stratigraphic units in the Saga area.

Formation	Primary Minerals	Alteration Minerals
Dajiling Formation	Quartz, Plagioclase, Mica	Sericite, Limonite, malachite
Ziqu Formation	Calcite, Quartz	Chlorite, Hematite
Zhongba Formation	Calcite	Limonite, Silicification
Gangzhutang Formation	Quartz, Mica	Sericite, Epidote
Gazhale Formation	Quartz, Plagioclase, Mica	Sericite, Limonite
Kagongyan Formation	Pyroxene, Plagioclase	Sericite, Chlorite
Yaquyan Formation	Quattez, Clay minerals	Chlorite, Limonite
Niuku Formations	Quattez, Clay minerals	Chlorite, Limonite
Tunjuri Formation	Calcite, Quartz,	Sericite, Limonite
Jipuyan Formation	Olivine, Pyroxene, Plagioclase	Sericite, Chlorite
Jiabula Formation	Calcite, Quartz	Limonite, Silicification
Angren Formation	Quartz	Sericite, Limonite
Chuangde Formation	Quattez, Clay minerals	Limonite
Zongzhuo Formation	Quartz, Plagioclase	Sericite, Epidote
Zezuweng Formation	Quartz, Mica	Sericite, Chlorite
Dasangyan Formation	Quattez, Clay minerals	Limonite
Danga Formation	Quartz	Silicification
Quaternary Alluvium	Quartz, Feldspar fragments	Limonite
Granite Diabase	Quartz, Feldspar	Sericite, Limonite, Hematite

); the Ziqu Formation, consisting mainly of marble and quartz schist, with common calcite and quartz, and alteration features such as chloritization and hematitization; the Permian Zhongba Formation, composed predominantly of dolomite, where calcite is the principal mineral and silicification and limonitization are locally developed; the Gangzhutang Formation, dominated by phyllitic slate, with a mineral assemblage of quartz and sericite, locally affected by sericitization and epidotization; and the Gazhale Formation, composed of phyllite and phyllitic slate, commonly containing quartz, feldspar, and sericite, and pervasively affected by hydrothermal alteration, including sericitization and limonitization. The Mesozoic strata are relatively complex. Triassic units include the Kagongyan Formation, composed mainly of basalt, with relict pyroxene and plagioclase, locally altered to chlorite and sericite; the Yaqu and Niuku Formations, dominated by shale and quartz sandstone, with a mineral assemblage of quartz and clay minerals, and alteration features such as chloritization and limonitization; the Tunjuri Formation, consisting of phyllite and limestone, with common quartz and calcite, and locally affected by sericitization and limonitization; the Jipuyan Formation, dominated by gabbro and diabase, where primary minerals such as olivine, pyroxene, and plagioclase are present, the latter frequently altered to sericite, illite, or chlorite, accompanied by hematite and goethite formation; the Jiabula Formation, composed mainly of sandstone and limestone, with quartz and calcite, and characterized by silicification and limonitization; the Angren Formation, dominated by quartz sandstone, primarily composed of quartz, with associated sericitization and limonitization; the Chuangde Formation, consisting of quartz sandstone and shale, with quartz and clay minerals and local limonitization; and the Zongzhuo Formation, composed mainly of quartz sandstone, retaining quartz and feldspar relics, and featuring alteration products such as sericite and epidote. The Cretaceous strata include the Zezuweng Formation, composed of phyllite and slate, with quartz and mica, and commonly affected by sericitization and chloritization; the Dasangyan Formation, consisting of shale and quartz sandstone, with quartz and clay minerals and associated limonitization; and the Danga Formation, dominated by quartz sandstone, composed primarily of quartz and locally silicified. The Cenozoic strata are mainly represented by Quaternary alluvial deposits (Qh) (Figure 1). In addition, intrusive rocks are widely distributed in the region, including Neogene monzogranite and Cretaceous diabase. The monzogranite is primarily composed of quartz, K-feldspar, and plagioclase, commonly affected by sericitization and limonitization. The diabase contains pyroxene and plagioclase, and is frequently subjected to chloritization, hematitization, and goethitization. Hydrothermal alteration and mineralization are well developed throughout the region, with common features including malachite alteration, epidotization, chloritization, sericitization, hematitization, and limonitization.

2.2. Overview of Remote Sensing Data

The Sentinel-2 satellite [40,41,42,43], part of the European Union’s Copernicus Earth observation program, is equipped with a multispectral imager covering 13 spectral bands across the visible, near-infrared, and shortwave infrared regions. These bands offer spatial resolutions of 10 m, 20 m, and 60 m, respectively.

This study utilized Level-2A Sentinel-2 imagery acquired on 13 June 2024, with satisfactory overall data quality (Figure 2a). Although Level-2A products provide bottom-of-atmosphere reflectance data that have already undergone radiometric calibration and atmospheric correction, reconstruction of the low-resolution bands is still required during the image processing stage. In this study, the S2 Resampling tool provided by SNAP 11.0.0 software was employed to reconstruct the low-resolution Sentinel-2 bands to a spatial resolution of 10 m while preserving their spectral characteristics. For the subsequent spectral mixture analysis and remote sensing-based extraction of alteration anomalies, specific Sentinel-2 bands were excluded based on their primary functions: Bands 1, 9, and 10 are mainly designed for coastal aerosol, cirrus clouds, and water vapor detection, respectively; additionally, Bands 8 and 8a have similar central wavelengths, but Band 8a is more sensitive to rock alteration. Therefore, ENVI software (ENVI 5.3, Exelis Visual Information Solutions, Boulder, CO, USA) was used in this study to synthesize Sentinel-2 imagery, excluding Bands 1, 8, 9, and 10, which are primarily designed for atmospheric corrections and thus not suitable for geological analysis. A masking procedure was applied to reduce non-geological interference, such as Quaternary sediments, snow, and rainfall in the study area. The resulting preprocessed Sentinel-2 image (Figure 2b) served as the basis for subsequent analysis in this study.

2.3. Overview of Spectral Data

In the process of geological mineral exploration, the presence of altered rocks in a region does not necessarily indicate mineralization. However, extensive and intense wall-rock alteration is typically associated with large or super-large ore deposits. There are notable differences in mineral composition and lithology between unaltered and altered rocks, which result in distinct variations in their reflectance spectral characteristics. These spectral differences form the theoretical basis for extracting alteration information through remote sensing. In the Saga area, various near-ore wall-rock alteration minerals are developed, including malachitization, epidotization, chloritization, sericitization, hematitization, and limonitization. These alteration minerals are generally rich in Fe³⁺, Fe²⁺, hydroxyl groups (OH⁻), or carbonate groups (CO₃²⁻), which give rise to diagnostic spectral absorption features that reflect their mineralogical composition. These spectral features can serve as important indicators for remotely sensing alteration anomalies and delineating potential ore-targeting areas. This study utilizes the reflectance spectra of representative minerals from the USGS spectral library (Figure 3) to analyze the locations of diagnostic absorption features and to extract the corresponding alteration anomaly information for each key mineral [44].

3. Research Methods

3.1. Sequential Maximum Angle Convex Cone

Several models have been developed for spectral mixture analysis, among which the Linear Spectral Unmixing (LSU) model is the most widely applied due to its simplicity and ease of implementation [45]. The LSU technique separates the spectra of each remote sensing data image by utilizing the research results, such as the combination of feature spectra for the characteristics of the mixed image elements. In the linear model of mixed pixels, the gray value of any pixel in a given band reflects the co-reflections in that band from all places inside it, and its magnitude is linearly related to the area of the feature. The result of linear spectral separation is usually expressed as a series of gray-scale images of end-element spectra, also known as abundance images, in which the value of the image element indicates the proportion of the end-element spectra in the image element. The selection of end-elements is the prerequisite and key to the linear hybrid image decomposition method, and the type, number, and value of end-elements determine the success or failure of the hybrid image decomposition technique. The end elements are selected on the spectral image to represent the spectrum of the pure feature surface. The Sequential Maximum Angle Convex Cone (SMACC) method is a commonly used end-element extraction method for the Linear Spectral Unmixing model, which can simultaneously extract the end elements and their abundance proportion of each feature from the mixed image [46,47,48].

The SMACC model’s fundamental assumption is that a surface comprises a limited number of spectral endmembers. In a three-dimensional data cube representing remote sensing imagery—consisting of spatial dimensions (x-axis and y-axis) and spectral bands—the number of spectral bands is denoted by L, and the number of spectral endmembers by p. Each pixel within this cube can be interpreted as a vector in an L-dimensional spectral space, with each dimension corresponding to a specific spectral band. Thus, the spectral signature of each pixel is expressed as a linear combination of p spectral endmember vectors. Consequently, the original remote sensing image data can be mathematically described by:

$$R_i={\sum }_{k=1}^N{M}_{ik}{S}_k+{\mathit{\epsilon }}_i, i=1, 2,\cdots ,L$$

(1)

In this equation, M_ik denotes the reflectance of the k-th endmember at the i-th spectral band, and S_k represents the fractional abundance of the k-th endmember, with the abundance vector defined as a = {a₁, a₂, …, aₚ}ᵀ. The noise term ε_i captures additive random disturbances in the remotely sensed image data [49,50].

There are two assumptions about the endmembers: nonnegativity and sum-to-one. Mixing matrix M and abundance matrix S are required for nonnegativity (a ≥ 0), and the sum-to-one constraint is used for the pixel fraction. The sum of all endmembers in a single pixel is one, i.e., ∑_q=1^ps(q) = 1. The simplex is represented as Sx = {x ∈ R^L: x = Mα, α ≥ 0, ∑_q=1^ps(q) = 1.} when Noise is zero, and the simplex shape is like a convex cone Cp = {r ∈ R^L: r = MS, α ≥ 0, ∑_q=1^ps(q) = 1, γ ≥ 0} [49,50].

As illustrated in Figure 4, the algorithm operates within the simplex Sx (where γ = 1), defined by a convex combination of spectral signatures. In the first iteration, the data points are projected in the direction of f₁, which emphasizes the contribution of the first endmember m_a. The resulting projection identifies m_a as the extreme point in that direction. In the second iteration, the data are re-projected along a new orthogonal direction, f₂, allowing the algorithm to extract the second endmember m_b. This iterative procedure continues, projecting the residual data in successively orthogonal directions until no additional distinct endmembers can be found.

3.2. Box-Counting Fractal Method

Mandelbrot introduced the concept of “fractal theory” in 1983. Fractal theory has experienced widespread application and continuous innovation within the quantitative analysis domain of Earth sciences [51,52,53]. Alteration anomalies derived from remote sensing imagery typically exhibit complex and irregular spatial patterns, rendering traditional Euclidean geometry methods inadequate for accurately characterizing their spatial distribution. However, these alteration anomalies often display pronounced fractal self-similarity as an intrinsic feature. Therefore, fractal theory can effectively quantify the spatial self-similarity of alteration anomalies by calculating their fractal dimensions, providing a robust measure for characterizing the complexity of their spatial distributions. Several approaches have been developed for calculating fractal dimensions, including box-counting dimension (capacity dimension) [54,55], information dimension [56,57], similarity dimension [58,59], correlation dimension [60,61], and Hausdorff dimension [62,63]. Among these, the box-counting dimension method has become the most widely adopted approach due to its straightforward theoretical foundation and computational simplicity.

The implementation of the box-counting method involves overlaying the study area with a two-dimensional square grid of varying box sizes (side length = r) and counting the number of boxes, N(r), that contain alteration anomaly information at each scale. The relationship between the number of boxes N(r) and box size r follows a power-law distribution, as expressed by Eq:

N(r) = Cr^−D

(2)

where C is a constant, and D is the box dimension subdimensional value. Take the logarithm of each side of the above equation:

lnN(r) = −Dlnr + lnc

(3)

Based on Equation (2), a log r − log N(r) plot is constructed and fitted using the least-squares method. The slope of the fitted line represents the box-counting fractal dimension, while the coefficient of determination (R²) is used to evaluate the goodness of fit. An R² value closer to 1 indicates a better fit and stronger agreement between the data and the power-law model. In this study, the fractal dimensions of remote sensing-derived alteration anomalies in the study area were calculated using the box-counting method described above, and corresponding fractal dimension contour maps were subsequently generated. The specific procedures are as follows: first, an initial observational scale (r) was defined, and two-dimensional orthogonal grids with side lengths of r = r₀, r₀/2, r₀/4, and r₀/8 were used to overlay the study area (Figure 5) sequentially. For each scale, the number of grid cells N(r) containing alteration anomaly information was recorded. Next, a log-log plot of r versus N(r) was constructed, and the box dimension (D), along with its coefficient of determination (R²), was obtained through least-squares linear regression. This approach enabled a quantitative characterization and evaluation of the spatial distribution of remote sensing alteration anomalies.

4. Results and Discussion

4.1. Spectral Mixture Analysis

The preprocessed imagery was first subjected to Minimum Noise Fraction (MNF) transformation using ENVI software to perform noise reduction and dimensionality reduction, after which the SMACC method was applied to extract endmember spectra and abundance information. The processed imagery was decomposed into abundance images corresponding to nine distinct land-cover endmembers (Figure 6) and one shadow image, with the total abundance summing to one. Additionally, spectral vectors were generated for each of the nine endmember components (

Table 2. Spectral vectors of nine endmember waveforms from remotely sensed images of the study area.

	Band 2	Band 3	Band 4	Band 5	Band 6	Band 7	Band 8a	Band 11	Band 12
1	4912	5948	6560	6675	6676	6734	6808	7893	6424
2	6512	6712	6640	5651	5453	5157	4789	2267	2162
3	3444	2256	2268	3435	3367	3691	4311	6460	6342
4	1675	2190	2122	2777	4259	4604	4920	4181	3007
5	6572	6916	6936	4633	4448	4396	4204	2853	2534
6	3856	4012	4372	3648	3618	4031	4278	5825	6136
7	2440	3432	4272	4548	4702	4913	4987	7146	6375
8	4524	5092	5044	5780	5638	5435	4974	2311	1975
9	2144	2636	2960	3083	3133	3325	3489	6116	5621

). In the experiment, these nine endmember spectral vectors were compared and identified against standard spectra from a reference spectral library. By combining this spectral comparison with their spatial distribution patterns in the abundance images, endmember component 3 was identified as vegetation, while endmember components 4 and 6 were classified as other interference features.

After determining the reflectance and abundance values of vegetation and interfering endmembers, spectral unmixing was performed for each spectral band using a linear mixture decomposition model. Taking Band 2 (B2) as an example, the abundance values of the identified interfering endmembers 3, 4, and 6 (denoted as F₃, F₄, and F₆) were multiplied by their respective spectral reflectance values in B2 and summed to obtain a composite image representing the contribution of vegetation-related interference. This composite reflectance was subtracted from the original radiance value of B2 (DN₂) to yield a reflectance image of the non-vegetation components. The resulting image was then divided by the proportion of the remaining abundance (1 − F₃ − F4 − F6) to reconstruct the reflectance values of the rock and soil endmembers, thereby effectively removing the influence of interfering endmembers in this band. This process can be represented by the following equation:

$${DN}_{Last}={\displaystyle \frac{{DN}_i-\sum _{j=1}^n{DN}_{ij}{F}_j}{1-\sum _{j-1}^n{DN}_{ij}{F}_j}}$$

(4)

where DN_Last denotes the reconstructed reflectance value of the pixel, i represents the band number, j is the endmember index, and F_j is the abundance value of the jth endmember. This method enables the compensation and reconstruction of rock and soil spectral components across all bands in the remote sensing imagery of the study area (

Table 3. Calculation formula for reconstructed images of the study area.

Band	Calculation Formula
2	(DN1 − F3 × 3444 − F4 × 1675 − F6 × 3856)/(1 − F3 − F4 − F6)
3	(DN2 − F3 × 2256 − F4 × 2190 − F6 × 4012)/(1 − F3 − F4 − F6)
4	(DN3 − F3 × 2268 − F4 × 2122 − F6 × 4372)/(1 − F3 − F4 − F6)
5	(DN4 − F3 × 3435 − F4 × 2777 − F6 × 3648)/(1 − F3 − F4 − F6)
6	(DN5 − F3 × 3367 − F4 × 4259 − F6 × 3618)/(1 − F3 − F4 − F6)
7	(DN6 − F3 × 3691 − F4 × 4604 − F6 × 4031)/(1 − F3 − F4 − F6)
8a	(DN7 − F3 × 4311 − F4 × 4920 − F6 × 4278)/(1 − F3 − F4 − F6)
11	(DN8 − F3 × 6460 − F4 × 4181 − F6 × 5825)/(1 − F3 − F4 − F6)
12	(DN9 − F3 × 6342 − F4 × 3007 − F6 × 6136)/(1 − F3 − F4 − F6)

). Following spectral mixture analysis, rocky terrain in sparsely vegetated mountainous areas became distinctly apparent. Additionally, in densely vegetated regions, the vegetation signal was effectively suppressed without excessive masking, thereby preserving alteration information on rock surfaces and enhancing the reliability of subsequent alteration anomaly extraction in this study.

4.2. Remote Sensing Alteration Anomaly Information Extraction

According to previous studies, PCA and band ratio methods are commonly utilized to extract remote sensing alteration information. In this study, PCA was applied to identify alteration anomalies associated with iron-staining and hydroxyl-bearing minerals (Al–OH and Mg–OH), based on their characteristic spectral absorption features (

Table 4. Table of principal component analysis eigenvectors of sentinel-2 imagery in the saga area.

Type	Eigenvector	Band 2	Band 4	Band 8A	Band 11
Iron-staining	PC1	0.313681	0.478547	0.517353	0.636351
	PC2	0.617418	0.340468	0.160370	−0.690767
	PC3	−0.568580	0.785637	−0.181369	−0.163086
	PC4	−0.443977	−0.194549	0.820812	−0.302163
Type	Eigenvector	Band 6	Band 8A	Band 11	Band 12
Al-OH	PC1	−0.480088	−0.506195	−0.516806	−0.49618
	PC2	0.468644	0.504798	−0.674377	−0.26602
	PC3	−0.419601	0.146771	−0.480054	0.756269
	PC4	0.611411	−0.68367	−0.218348	0.333311
Type	Eigenvector	Band 2	Band 8A	Band 11	Band 12
Mg-OH	PC1	−0.410902	−0.526081	−0.537109	−0.515667
	PC2	0.571009	0.46045	−0.619561	−0.279426
	PC3	0.543775	−0.362285	0.491561	−0.575698
	PC4	0.457622	−0.61642	−0.293304	0.56972

). Additionally, the band ratio method was employed to detect carbonate alteration anomalies by exploiting the distinct reflectance characteristics of carbonate minerals in Bands 7 and 12.

Iron-stained minerals (such as hematite, limonite, and goethite) exhibit characteristic absorption features in the spectral ranges of 0.48–0.51 μm and 0.85–0.89 μm, which correspond to Band 2 and Band 8A of Sentinel-2 imagery. Additionally, they display strong reflectance features in the ranges of 0.65–0.70 μm and 1.52–1.70 μm, corresponding to Band 4 and Band 11, respectively. Therefore, Bands 2, 4, 8A, and 11 were selected as the optimal band combination for extracting iron-staining alteration anomalies. The principal component associated with this anomaly is characterized by Band 2 and Band 8A having contribution coefficients with the same sign and both being negative. Analysis of the transformed principal component eigenvectors indicates that information related to iron-staining anomalies is primarily concentrated in the third principal component (PC3). As such, PC3 was selected as the principal component for extracting iron-staining alteration information.

OH group alteration minerals primarily include Al–OH minerals (such as kaolinite, muscovite, and alunite) and Mg–OH minerals (such as chlorite, epidote). Al–OH minerals exhibit reflective spectral features in the wavelength range of 1.60–1.70 μm, corresponding to Sentinel-2 Band 11, and absorption features near 2.20 μm, corresponding to Sentinel-2 Band 12. Consequently, Bands 6, 8a, 11, and 12 were selected as the optimal band combination for identifying Al–OH mineral alteration anomalies. PCA has then performed using these selected bands (Bands 6, 8a, 11, and 12). The principal component related to Al–OH anomalies shows positive and identical contribution coefficients for Bands 8a and 11, while Bands 11 and 12 exhibit opposite signs. Examination of the transformed eigenvectors indicated that the inverse of principal component 4 (–PC4) best matched these criteria (Table 3). Therefore, –PC4 was utilized as the principal component for extracting Al–OH mineral alteration anomalies in this study.

Mg–OH minerals exhibit an increasing reflectance trend in the wavelength range of 0.70–1.82 μm, corresponding to Bands 5–11 of Sentinel-2 imagery, and display characteristic absorption near 2.32 μm, corresponding to Sentinel-2 Band 12. Accordingly, Bands 2, 8a, 11, and 12 were selected as the optimal band combination for extracting Mg–OH mineral alteration anomalies. In the PCA, the principal component associated with Mg–OH anomalies typically show positive and identical coefficient signs for Bands 2 and 11 and opposite signs for Bands 11 and 12. Based on these criteria, eigenvector PC3 was selected as the principal component for identifying Mg–OH mineral alteration anomalies.

Carbonate minerals (such as calcite and dolomite) typically exhibit prominent reflectance peaks around 0.7–0.8 μm and 1.7–1.8 μm, corresponding to Bands 7 and 12 of Sentinel-2 imagery, respectively. Based on multiple experimental analyses, a hybrid band ratio method using Band 12/Band 8a and Band 7/Band 4 was adopted in this study. This mixed-ratio approach enhances the diagnostic spectral signatures of carbonate minerals, thereby effectively extracting associated alteration anomaly information.

After extracting the characteristic principal components or hybrid band-ratio images representing the four alteration types, we calculated the mean (μ) and standard deviation (σ) of the alteration-type images. Pixels with values greater than μ + Nσ were classified as remote-sensing alteration anomalies. The multiplier N was set to 3 for iron-staining, Al–OH and Mg–OH anomalies, and 2 for carbonate anomalies (

Table 5. Statistical summary of alteration anomaly values for different alteration types in the Saga area.

Type	Mean	Standard Deviation	Anomaly Value
Iron-staining	0	114.79	344.37
Al-OH	0	70.09	210.27
Mg-OH	0	140.50	421.5
Carbonate	1.3	1.35	4

). The final Sentinel-2 alteration anomaly map is shown in Figure 7.

4.3. Characterization of Alteration Anomalies in Remote Sensing Using Fractal Dimensions

The fractal dimension provides a useful indicator for locating ore deposits. In this study, box-counting fractal analysis was applied to four alteration-anomaly layers derived from Sentinel-2 imagery: iron staining, Al–OH, Mg–OH, and carbonate. This analysis aimed to pinpoint the most promising mineralization sites in the study area and provide theoretical guidance for future exploration efforts. Four two-dimensional orthogonal grids with cell sizes of 6.123 km, 3.062 km, 1.531 km, and 0.765 km were successively superimposed on the study area (Figure 5). For each grid scale, the number of square cells N(r) containing the respective alteration anomaly was recorded. The natural logarithm of cell size, ln r, was plotted on the x-axis and ln N(r) on the y-axis; least-squares regression lines were then fitted separately for each alteration type. The slope of each regression line yields the box-counting dimension D. The statistical results are summarized in

Table 6. Statistical parameters for fractal dimension calculations of various alteration anomalies in the saga area.

Type	Fractal Dimension				Type	Fractal Dimension
	r/km	N(r)	lnr	LnN(r)		r/km	N(r)	lnr	LnN(r)
Iron- staining	6.123	12	5.77455	3.46574	Al-OH	6.123	11	5.42935	3.46574
	3.062	35	4.74493	4.15888		3.062	33	4.60517	4.15888
	1.531	115	3.55535	4.85203		1.531	100	3.49651	4.85203
	0.765	322	2.48491	5.54518		0.765	228	2.3979	5.54518
Mg-OH	6.123	12	5.54908	3.46574	Carbonate	6.123	10	4.61512	3.46574
	3.062	34	4.7362	4.15888		3.062	24	4.00733	4.15888
	1.531	114	3.52636	4.85203		1.531	55	3.17805	4.85203
	0.765	257	2.48491	5.54518		0.765	101	2.30259	5.54518

, and the ln N(r)–ln r regression plots are shown in Figure 8.

Using the fractal method described above, fractal dimensions for each alteration type were calculated over the scale range of 0.765–6.123 km. The results are as follows: the iron-staining anomalies have an overall fractal dimension of 1.59541 with R² = 0.9968; the Al–OH anomalies, 1.47198 with R² = 0.9921; the Mg–OH anomalies, 1.50074 with R² = 0.9916; and the carbonate anomalies, 1.12053 with R² = 0.9925. The highest fractal dimension value of the iron-stained alteration in the study area indicates significant spatial structural complexity. This complexity may be closely related to mineralization centers formed by the oxidation of metal sulfides or the intersection of fracture sites. It is presumed to be a result of the main metallogenic period. Therefore, areas with high-D zones should be prioritized as core targets for exploration. The similar fractal dimensions of the Al–OH and Mg–OH anomalies imply comparable spatial complexities, likely reflecting different stages of the same mineralizing system; high-D areas delineate the probable boundaries of this system. By contrast, the lower fractal dimension of the carbonate anomalies points to a more uniform spatial distribution characteristic of peripheral or late-stage alteration zones. All alteration types exhibit R² > 0.99, confirming pronounced statistical self-similarity within the selected scale domain.

The fractal dimension statistics of the previously described division grid were calculated to determine the fractal dimension value of the etching anomalies within each grid range. Each grid was assigned a fractal dimension value based on its center. Using Surfer 23 software, the Kriging interpolation method was then employed to create contour maps depicting the various alteration anomalies. The resulting contour map illustrates the fractal dimension information for different alteration anomalies, as shown in Figure 9.

Overall, the fractal characteristics of the different alteration anomalies reveal their spatial complexity and genetic mechanisms. When combined with the spatial coupling between each alteration type and existing mineralization sites, these features constitute a valuable basis for regional prospecting, offering scientific support for identifying potential mineralization targets. To integrate the fractal-dimension contour maps of the various alteration types in the Saga area, we normalized each dataset to prevent the spatial characteristics of any one alteration type from being masked by large absolute values during a simple arithmetic overlay. This normalization effectively eliminated the problem of subdued anomaly zones in the composite layer that would otherwise arise from disproportionately high values in a single alteration map. The resulting composite fractal-dimension contour map shows that high-value zones are concentrated mainly within the Silurian–Ordovician Dajiling Formation and the Jurassic Zongzhuo Formation, exhibiting a conspicuous concentric-diffusion pattern. Based on the distribution of previously identified mineralization sites, areas with composite fractal values exceeding 0.7 were designated as high-potential mineralization zones, and two additional prospective targets were delineated outside the known mineralization sites (Figure 10).

4.4. Results of Field Survey and Laboratory Analysis

Field geological investigations coupled with laboratory mineralogical analyses are critical for validating remote sensing interpretation results. Based on the interpreted remote sensing anomaly data, systematic field geological route surveys and laboratory-based mineralogical characterizations were carried out in the prospective metallogenic area I. The Dagiling Formation constitutes the primary strata in this area (Figure 10). Field investigations identified several quartz vein outcrops near regions of remote sensing high-value anomalies, displaying evident alteration characteristics, including prominent malachitization and chalcopyrite mineralization on the surface of some quartz veins. Collected quartz vein samples were subsequently prepared into thin sections and examined under transmitted and reflected light microscopy, revealing diverse mineralization types such as copper mineralization (Figure 11a), malachitization (Figure 11b), sericitization (Figure 11c), pyritization (Figure 11d), limonitization (Figure 11e), and galena mineralization (Figure 11f). Structurally, the rocks exhibit brecciated, veined, stockwork, disseminated veinlet, and massive textures, with disseminated veinlet, densely disseminated, and brecciated textures being the most common. Disseminated veinlets and densely disseminated structures predominantly occur in stratiform-like ore bodies, whereas brecciated structures mainly develop in vein-type ore bodies. Additionally, disseminated limonitization, chloritization, and speckled limonitization are evident in foliated marbles, with banded limonitization aligning along foliation observed locally. These field observations and microscopic results closely align with the lithological characteristics of the Dagiling Formation previously analyzed in the remote sensing anomaly regions. The loosely structured, highly fractured nature and abundance of easily altered minerals in the Dagiling Formation facilitate hydrothermal fluid infiltration, migration, and mineralization, leading to the accumulation of multiple alteration anomalies identified. These validation findings not only confirm the reliability of the remote sensing methodology applied in this study but also provide essential groundwork for subsequent verification studies in other prospective metallogenic areas.

5. Conclusions

• Introducing spectral mixture analysis into the study area enables the separation of reflectance and abundance values for the various interference components contained in mixed pixels. Spectral unmixing and endmember reconstruction enhance the detectability of subtle mineralization-related alteration anomalies in the remote-sensing imagery. By accurately quantifying the spectral contribution of each surface feature, this approach prevents the loss of alteration signatures that often occurs with conventional masking techniques due to over-correction, and it markedly improves both the accuracy and completeness of alteration information extraction under diverse surface-cover conditions.
• The fractal dimensions of the various remote-sensing alteration anomalies were determined using fractal analysis, thereby quantitatively characterizing their spatial self-similarity. The iron-staining anomalies have a fractal dimension of 1.59541 with R² = 0.9968; the Al–OH anomalies, 1.47198 with R² = 0.9921; the Mg–OH anomalies, 1.50074 with R² = 0.9916; and the carbonate anomalies, 1.12053 with R² = 0.9925. Notably, all alteration types exhibit R² values greater than 0.99, confirming that the alteration anomalies in the study area display pronounced statistical self-similarity within the selected scale range.
• The high-potential mineralization zones delineated in the fractal-dimension contour map of alteration anomalies shows a strong spatial correspondence with known mineralization sites. In addition, two new prospective zones were identified on the periphery of the existing mineralization sites. These findings furnish a sound theoretical basis and clear exploration priorities for the next stage of prospecting in the study area.