Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries–Matusita (J–M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.


Introduction
Numerous studies have demonstrated the ability to exploit the spectral features of minerals and rocks from the reflected solar spectra for lithological mapping and mineral exploration.Based on the spectral features, from visible to near-infrared (VNIR) wavelengths, some iron-bearing minerals, such as goethite and hematite, have been found in prior research to be relatively easy to distinguish [1,2].Because of the fundamental vibrations of Al-OH, Mg-OH, and CO 3  2− bonds in shortwave infrared (SWIR) wavelengths, many alteration minerals such as carbonates, sulfates, hydroxides, and oxides have been successfully identified and mapped using remote sensing methods [1][2][3][4].In thermal infrared (TIR) wavelengths, igneous rocks with relatively high SiO 2 contents have diagnostic emission spectral features due to the vibration of the Si-O bond, based on which, some rock indices have been constructed, such as the sulfuric acid rock index and the carbonate rock index [5][6][7].Spectral-feature-based approaches for lithological identification mainly focus on the conversion and enhancement of spectral features, such as principal component analysis (PCA), spectral angle mapping (SAM), band ratio (BR), relative absorption band depth (RBD), false color composite (FCC), matched-filtering, and combinations of these methods [8][9][10].
imagery resampled to 15 m using the panchromatic band was used to derive texture images and for classification procedures.To select optimal combinations of spectral and textural vectors, the Jeffries-Matusita (J-M) distance was calculated for pairwise rocks.A large J-M distance value indicates a higher class of separability.Then, textural and spectral features were integrated using the SVM model to improve lithological classification.The results were evaluated based on the confusion matrix.

Study Area
The study area (Figure 1) is located within latitudes 38°10′ N and 38°45′ N and longitudes 98°45′ E and 99°30′ E, in the northern Qinghai Province of western China, which lies in the Chi-lien Mountains of the northern of Qinghai-Tibet Plateau.The region has a plateau continental climate and an approximate mean elevation of 4200 m, resulting in abundant snow and ice cover for most of the year.Vegetation in this area is mainly low grass.The summer season in the area occurs from June to August with minimal snow and ice cover.Despite the increasing vegetation cover associated with summer conditions, geological materials are best exposed during this time frame [11,49].Therefore, this is the best period for remote sensing observations.Based on fieldwork and remote sensing images, the study area was selected at locations where rocks were relatively well exposed, as shown in the compiled lithological map (Figure 2a), however, vegetation was still spotted, as visible in the false color composition (Figure 2b).The main rock types in this area include gabbro, serpentinite, granite, and quartzose rock, which are well exposed and are less covered by vegetation.In this area, gabbro, serpentinite, and granite are all intrusive rocks.Gabbro and serpentinite rocks are both the major components of mafic-ultramafic rocks, and are seldom distributed in lower volcanic groups of Ordovician age.Granitoids are the representative granite rocks, which are related to rare earth metals, the origin of nonferrous metals, and radioactive element ore deposits [11].Sandstone is the major type of quartzose rock in this area and is mainly distributed in the lower Ordovician sandstone-slate group.Carbonate rocks are not widely exposed in the study area.Based on fieldwork and remote sensing images, the study area was selected at locations where rocks were relatively well exposed, as shown in the compiled lithological map (Figure 2a), however, vegetation was still spotted, as visible in the false color composition (Figure 2b).The main rock types in this area include gabbro, serpentinite, granite, and quartzose rock, which are well exposed and are less covered by vegetation.In this area, gabbro, serpentinite, and granite are all intrusive rocks.Gabbro and serpentinite rocks are both the major components of mafic-ultramafic rocks, and are seldom distributed in lower volcanic groups of Ordovician age.Granitoids are the representative granite rocks, which are related to rare earth metals, the origin of nonferrous metals, and radioactive element ore deposits [11].Sandstone is the major type of quartzose rock in this area and is mainly distributed in the lower Ordovician sandstone-slate group.Carbonate rocks are not widely exposed in the study area.

Dataset and Processing
One image of Landsat 8 OLI data (LC81340332014244LGN00) with few clouds provided by the USGS EROS data center (Sioux Falls, SD, USA) [46] was used in this study, and is detailed in Table 1.The data were collected on 1 September 2014, close to the summer season (from June to August) to minimize snow and ice cover and maximize the exposure of rocks and minerals.The images acquired are Level 1 digital number data products, with radiation calibration and geometric correction methods previously applied.The data were processed as follows: (i) VNIR-SWIR images (band 1 to band 7, 30 m resolution) were radiometrically calibrated to obtain the top of atmosphere reflectance

Dataset and Processing
One image of Landsat 8 OLI data (LC81340332014244LGN00) with few clouds provided by the USGS EROS data center (Sioux Falls, SD, USA) [46] was used in this study, and is detailed in Table 1.The data were collected on 1 September 2014, close to the summer season (from June to August) to minimize snow and ice cover and maximize the exposure of rocks and minerals.The images acquired are Level 1 digital number data products, with radiation calibration and geometric correction methods previously applied.The data were processed as follows: (i) VNIR-SWIR images (band 1 to band 7, 30 m resolution) were radiometrically calibrated to obtain the top of atmosphere reflectance data; (ii) the Fast Line-of-Sight atmospheric analysis of spectral hypercubes (FLAASH) models were conducted for atmospheric correction to obtain surface reflectance data; (iii) the images were fused with the panchromatic band (band 8, 15 m resolution) to a spatial resolution of 15 m.The final reflectance images from VNIR to SWIR wavelengths, with a spatial resolution of 15 m, were used to derive texture images for lithological classification.All the procedures of data processing in this section were conducted in ENVI (ENVI5.2SP 1, ITT Visual Information Solutions, Boulder, CO, USA, 2015).Four rock types are discussed in this paper: gabbro, serpentinite, granite, and quartzose rocks, which are widely distributed within the earth's crust and are well exposed in the study area.Rocks or minerals that are much covered by vegetation are not addressed in this paper, as the texture and spectral information would be concealed or impaired by vegetation.In addition, some samples of these rocks were selected based mainly on the geological map (Figure 2a).In addition, some remote sensing indices derived from the Advanced SpaceborneThermal Emission and Reflectance Radiometer (ASTER, National Aeronautics and Space Administration, Washington, DC, USA) imagery (i.e., b12 × b14 3 /b13 4 , b13/b12) were also used as supplementary information for selecting samples of gabbro and quartzose rock.In fact, the method of choosing rock samples in this study is the same as that used in previous studies [11,49], and is detailed in Ding et al. [48].

Box-Counting Dimension Method to Extract Textural Features
The concept of fractal geometry, which was initially proposed by Mandelbrot [50], provides a simple method to describe the irregularities of complex objects and objects with self-similar characteristics at different scales (spatial, temporal, or otherwise) in nature.Remote sensing images are not only spectrally and spatially complex, but they often exhibit certain self-similarities at different scales [51].Therefore, increasing attention has been paid to fractal geometry in the remote sensing community.Fractal models have been used in a variety of image processing and pattern recognition applications [52].For example, they have been used to describe image texture and to segment various types of images [30,31,35,53].In the past decades, fractal geometry has been one of the most popular techniques for texture extraction because of its robustness in image scaling [30,31,41,54].Fractals characterize the roughness or geometric complexity of surface features in remote sensing images.Roughness or geometric complexity, which is related to surface morphology, plays an important role in improving image understanding and classification.Applications of fractal models to image analysis mainly depend on the estimation of the fractal dimension (FD) [52].More generally, the more irregular an object is, the higher its FD value.Thus, FD value is intimately linked to the concept of roughness or geometric complexity.In this way, different surface objects may have characteristic textures or roughness that could be measured by different FD values.In this study, the box-counting method devised by Goodchild [54] was used to calculate the fractal dimension.This method divides an area into regular boxes with the same box edge length.Here, M is the measured object, ε is the box edge length, M ε (C) and C is the lowest number of boxes in the grid divided curve and scales, respectively, and FD is the fractal dimension of M.
The value of FD ranges from 1.0 to 2.0.To extract the fractal dimension of each pixel in remote sensing images, a 3 × 3 moving window covering the neighboring pixels is used.In this way, a new layer is generated as the texture image.Each pixel in this layer is assigned an FD value.

Optimal Feature Combinations of Spectra and Textural Vectors
The texture images derived from seven spectral channels (VNIR-SWIR images) will yield hundreds of combinations of texture vectors for classification.Hence, feature selection has to be conducted before the image classification.Feature selection is a technique for selecting optimal combinations of vectors that consistently provide good performance in object classification [55].In this paper, feature selection is conducted by three steps, including (i) selecting some criterion to evaluate the effectiveness of input vector in lithological classification; (ii) selecting vector combinations which have the best performance (or achieve to the defined standard); and (iii) setting the selected vectors as the optimal features, then feature selection is accomplished.Given that we were initially interested in whether the incorporation of textural features would aid spectral features to improve classification accuracy, different combinations of texture images, fused with all seven spectral bands, were examined to select the optimal input feature vectors.
The effectiveness of input feature vectors in lithological classification can be measured by the separability between two classes.The J-M distance has been mostly used for evaluating the separability between two classes [56][57][58] and was applied here to select the optimal feature vectors.The larger the class separability of the input vectors, the greater the J-M distance.The J-M distance is the average distance between two class-density functions, which is calculated using Equations ( 2) and (3).
where i and j are the two classes being compared, m i and m j is the mean vector of class i and j, respectively, and C i and C j is the covariance matrix of the feature response of class i and j respectively.The value of J-M ranges from 0.0 to 2.0, and measures the pairwise class separability.A large J-M distance value indicates higher class separability.

Interference Elimination in Remote Sensing Images
A few clouds were present in the used images, and need to be considered, as shown in Figure 2a.In this figure, vegetation is shown in red, while clouds, snow, and ice are presented in white.Textural features are described in terms of spatial variability associated with the digital numbers (DNs) of local pixels.Hence, vegetation and clouds in the image would conceal or impair the textural information.To eliminate interference from vegetation, the normalized difference vegetation index (NDVI) was calculated using Equation (4).Based on the statistical characterization of vegetation pixels from the NDVI image, the threshold value for NDVI was set as 0.4 to mark off the vegetation pixels, which are colored green in Figure 3a.Clouds have a relatively high reflectance in the NIR wavelength (band 5).Based on the statistical characterization of cloud pixels from the NIR reflectance image, the threshold of 0.35-1.0 was set to mark off the cloud pixels, as shown in white in Figure 3b.The pixels falsely labeled as cloud were mainly distributed on the bright valley walls (e.g., in the northeast corner of Figure 3b).The bright valley in this study area is mostly covered by vegetation.Since a mask of vegetation pixels was also used before image classification, the pixels falsely labeled as cloud will not influence the results.

Support Vector Machine Classifier
SVM based on statistical learning theory (SLT) is one of the most popular learning algorithms for classification and has been intensively studied by Vapnik et al. [59][60][61][62].Unlike traditional SLT, which is based on structural risk minimization (SRM), SVM is a new universal learning theory based on Empirical Risk Minimization (ERM).Given its excellent learning and generalization ability, SVM has been widely employed in many fields, such as financial forecasting, image processing, face detection, and handwritten digit recognition [63][64][65][66][67].
The basic method of SVM classification can be generalized by the following two steps [64]: 1. Map all input vectors to a high-dimensional feature space based on the kernel function, which is either linear or nonlinear.2. Construct an optimal hyperplane based on support vectors to separate the data into two classes according to the theory of the least errors and maximal margin between the hyperplane and the closest training data.The training samples that are closest to the hyperplane are support vectors, whereas the others are uncorrelated for establishing the class divisions.
We define ( , ), ( , ), …, ( , ) as the input training samples, as input vectors, and (−1,1) as the decision value of the training sample for each class.In terms of support vectors, the decision rule defined by an optimal hyperplane classifying the input data is given by the following equation: where = ( , , … … ) represents the input, the vectors ( = 1,2, … … ) are support vectors, and are specific parameters of the hyperplane, and ( , ) is the kernel function used to construct machines with different types of decision surfaces in the feature space.
The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48,68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.

Support Vector Machine Classifier
SVM based on statistical learning theory (SLT) is one of the most popular learning algorithms for classification and has been intensively studied by Vapnik et al. [59][60][61][62].Unlike traditional SLT, which is based on structural risk minimization (SRM), SVM is a new universal learning theory based on Empirical Risk Minimization (ERM).Given its excellent learning and generalization ability, SVM has been widely employed in many fields, such as financial forecasting, image processing, face detection, and handwritten digit recognition [63][64][65][66][67].
The basic method of SVM classification can be generalized by the following two steps [64]: 1. Map all input vectors to a high-dimensional feature space based on the kernel function, which is either linear or nonlinear.

2.
Construct an optimal hyperplane based on support vectors to separate the data into two classes according to the theory of the least errors and maximal margin between the hyperplane and the closest training data.The training samples that are closest to the hyperplane are support vectors, whereas the others are uncorrelated for establishing the class divisions.
We define (X 1 , y 1 ), (X 2 , y 2 ), . . ., (X n , y n ) as the input training samples, X i R n as input vectors, and y i (−1, 1) as the decision value of the training sample for each class.In terms of support vectors, the decision rule defined by an optimal hyperplane classifying the input data is given by the following equation: where X = (x 1 , x 2 , . . . . . .x n ) represents the input, the vectors X i (i = 1, 2, . . . . . .N) are support vectors, a i and b are specific parameters of the hyperplane, and K (X, X i ) is the kernel function used to construct machines with different types of decision surfaces in the feature space.
The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48,68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.Seven texture images were derived from seven spectral bands of Landsat 8 data using the box-counting method.To examine the effectiveness of different texture images for lithological identification, the FD values of the rock samples were extracted from the seven texture images, and the mean FD value of each object is detailed in Figure 4. Figure 4 provides the following information: 1. On the whole, shows a remarkable contrast between serpentinite and other rocks.The serpentinite has the highest , which means that its surface morphology is rougher than that of other rocks.In 1 and TF2, the values for gabbro and serpentinite are close, while from 3 to 7, there is an obvious difference between the values for gabbro and serpentinite, which may result from the fact that texture images derived from different source data have a variable effect on rock identification.2. Regardless of the different texture images, different values for the four rock types exist because of their different surface roughness, which would assist rock identification.From TF2 to TF6, the values for gabbro and granite are almost the same, while for other pairwise rocks, the contrast is not immutable.
In conclusion, textural features measured by may aid in separating certain different types of rocks.Texture images derived from different spectral bands have different sensitivities for lithological identification.Different texture images have varying abilities to identify the same pairs of rocks.For example, TF2 is not sufficient to distinguish gabbro from serpentinite, but it can be used to distinguish granite from quartzose rock in this example.Chen et al. [69] demonstrated that using only a single textural measured by fractal dimension is not effective to characterize objects in an image.Hence, it is necessary to examine the ability of different texture images and combinations of them before image classification.

Feature Vector Selection
In this study, our aim is to explore whether the incorporation of textural features can assist in Figure 4 provides the following information: 1.
On the whole, FD shows a remarkable contrast between serpentinite and other rocks.The serpentinite has the highest FD, which means that its surface morphology is rougher than that of other rocks.In TF1 and TF2, the FD values for gabbro and serpentinite are close, while from TF3 to TF7, there is an obvious difference between the FD values for gabbro and serpentinite, which may result from the fact that texture images derived from different source data have a variable effect on rock identification.

2.
Regardless of the different texture images, different FD values for the four rock types exist because of their different surface roughness, which would assist rock identification.From TF2 to TF6, the FD values for gabbro and granite are almost the same, while for other pairwise rocks, the FD contrast is not immutable.
In conclusion, textural features measured by FD may aid in separating certain different types of rocks.Texture images derived from different spectral bands have different sensitivities for lithological identification.Different texture images have varying abilities to identify the same pairs of rocks.For example, TF2 is not sufficient to distinguish gabbro from serpentinite, but it can be used to distinguish granite from quartzose rock in this example.Chen et al. [69] demonstrated that using only a single textural measured by fractal dimension is not effective to characterize objects in an image.Hence, it is necessary to examine the ability of different texture images and combinations of them before image classification.

Feature Vector Selection
In this study, our aim is to explore whether the incorporation of textural features can assist in distinguishing different silicate rocks.Hence, different combinations of texture images fused with all seven spectral features are examined.A total of 127 combinations of texture and spectral vectors are possible, as detailed in Table 2.Then, the J-M distance is calculated between pairwise rocks to select the optimal combinations of input feature vectors, as shown in Figure 5. Figure 5 shows the feasibility of different vector combinations for lithological identification in the form of matrix.Each element (i, j) (here the i and j ranges from 1 to 7 and from 1 to 35, respectively) in the matrix indicates the vector combination in Series i and row j in Table 2.It needs to be noted that the element (0, 0) in the matrix (Figure 5) indicates the seven spectral vectors without textural features.The value of element (i, j) indicates the J-M distance between two classes.For example, element (3,4) indicates the vector combination in Series 3 and row 4 in Table 2, namely SF + TF126; the value of element (3,4) indicates the J-M distance between two classes calculated using the vector combination of SF + TF126.The matrices in Figure 5 were quantified using shades of color, which will help to visually distinguish the different performances of vector combinations in identifying pairwise rocks.
a The expression "SF + TF1-7" indicates the feature combination of seven spectral bands fused with TF1, TF2, TF3, TF4, TF5, TF6, and TF7.The same convention applies to the rest of the table contents.
2. It is clear that gabbro-serpentinite has the largest J-M distance, followed by serpentinite-granite, gabbro-granite, gabbro-quartzose rock, serpentinite-quartzose rocks and granite-quartzose rocks, which suggests that textural features are more useful for identifying granite and quartzose rocks.Table 3. Statistically significant J-M distance for the same number of vectors a .
To further examine the performance of texture images in identifying different pairs of rocks, the increase in class separability for the seven series that have statistically significant J-M distances for the same numbers of feature vectors (Table 3) were calculated, as shown in Figure 6.The increase in class separability is, in fact, the difference in value of J-M distance between the seven series with the seven spectral vectors.It is evident that the incorporation of textural vectors has a remarkable effect on the following pairs of rocks: serpentinite-quartzose rock, granite-quartzose rock, gabbroquartzose rock, and gabbro-granite.In particular, the textural vectors performed best on serpentinite-quartzose rock and granite-quartzose rock.For serpentinite-granite and gabbroserpentinite, which can be well distinguished when only spectral vectors are used, the increase in class separability is insignificant.Figure 5 provides the following information: 1.
For all six groups of pairwise rocks, the J-M distance increases significantly with the number of textural vectors, indicating that the incorporation of texture is useful for improving the identification of rocks.Moreover, for the same number of textural vectors, different combinations of them generate different J-M distances.The statistically significant J-M distances for the same number of vectors are detailed in Table 3.The textural vectors of TF4, TF7, and TF6 had better performance in identifying gabbro-serpentinite, while TF3, TF5, and TF1 were more effective in identifying gabbro-granite.The textural vectors of TF2 and TF3 were more effective for gabbro-quartzose rock; TF4, TF6, and TF1 for serpentinite-granite; TF4, TF5, and TF6 for serpentinite-quartzose rock; and TF6, TF2, TF3, and TF2 were more effective for identifying granite-quartzose rock.For all six pairs of rocks, the J-M distance has the highest value when all seven vectors are incorporated.

2.
It is clear that gabbro-serpentinite has the largest J-M distance, followed by serpentinite-granite, gabbro-granite, gabbro-quartzose rock, serpentinite-quartzose rocks and granite-quartzose rocks, which suggests that textural features are more useful for identifying granite and quartzose rocks.
To further examine the performance of texture images in identifying different pairs of rocks, the increase in class separability for the seven series that have statistically significant J-M distances for the same numbers of feature vectors (Table 3) were calculated, as shown in Figure 6.The increase in class separability is, in fact, the difference in value of J-M distance between the seven series with the seven spectral vectors.It is evident that the incorporation of textural vectors has a remarkable effect on the following pairs of rocks: serpentinite-quartzose rock, granite-quartzose rock, gabbro-quartzose rock, and gabbro-granite.In particular, the textural vectors performed best on serpentinite-quartzose SF + TF67 SF + TF235 SF + TF2367 SF + TF23567 SF + TF123567 SF + TF1-7 a Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock; the expression SF + TF1234567 indicates the feature combination of seven spectral features fused with TF1, TF2, TF3, TF4, TF5, TF6, and TF7.The statistical results indicate that integrating spatial textural features with spectral features yields an improvement in rock separability, especially for granite and quartzose rock, which may result in improved classification accuracy.To confirm this premise, the vector combinations that achieved the most significant J-M distances were compared with the classification results obtained using only spectral vectors.

Lithological Classification Using Selected Features
The texture and spectral images acting as input vectors were fused in the SVM classifier for lithological classification.Then, the Landsat 8 imagery was classified into four rock types, including gabbro, serpentinite, granite, and quartzose rock.The classification results are shown in Figures 7  and 8.The classification accuracy was calculated based on the confusion matrix and was presented in Tables 4 and 5.The results show that the classification incorporating textural features is noticeably improved compared to that using only spectral vectors.The overall accuracy and Kappa coefficient (κ) increased by 9.04% and 0.13%, respectively.Moreover, all four rock objects were better identified, especially granite and quartzose rocks, which had the highest increase in classification accuracy, confirming the results presented in Section 4.2.
Improved performance is achieved when the selected feature vectors are integrated in SVM for lithological classification.This can be attributed to two factors.First, texture features improve our understanding of images, as they characterize the roughness or geometric complexity of objects in the remote sensing image.Second, the optimal combination of feature vectors is selected to act as the input vectors in a classifier.However, the universality of this strategy needs to be confirmed further.The statistical results indicate that integrating spatial textural features with spectral features yields an improvement in rock separability, especially for granite and quartzose rock, which may result in improved classification accuracy.To confirm this premise, the vector combinations that achieved the most significant J-M distances were compared with the classification results obtained using only spectral vectors.

Lithological Classification Using Selected Features
The texture and spectral images acting as input vectors were fused in the SVM classifier for lithological classification.Then, the Landsat 8 imagery was classified into four rock types, including gabbro, serpentinite, granite, and quartzose rock.The classification results are shown in Figures 7 and 8.The classification accuracy was calculated based on the confusion matrix and was presented in Tables 4  and 5.The results show that the classification incorporating textural features is noticeably improved compared to that using only spectral vectors.The overall accuracy and Kappa coefficient (κ) increased by 9.04% and 0.13%, respectively.Moreover, all four rock objects were better identified, especially granite and quartzose rocks, which had the highest increase in classification accuracy, confirming the results presented in Section 4.2.robustness of this strategy, which would also limit the accuracy of image classification.Hence, for other regions or other rock types, more analysis and validation is necessary.Table 4. Confusion matrix and accuracy evaluation of classification results using support vector machine (SVM) with seven spectral vectors.Improved performance is achieved when the selected feature vectors are integrated in SVM for lithological classification.This can be attributed to two factors.First, texture features improve our understanding of images, as they characterize the roughness or geometric complexity of objects in the remote sensing image.Second, the optimal combination of feature vectors is selected to act as the input vectors in a classifier.However, the universality of this strategy needs to be confirmed further.Only four common rock types are discussed in this paper, due to the limited size of the study area.Larger areas with additional rock types should also be taken into consideration.The concept of texture has been successfully introduced in remote sensing science.Textural features help to better characterize the surface morphology of surface objects.In future studies, other surface parameters related to the physical or chemical properties of rocks should be considered for lithological identification, such as permittivity, thermal characteristics, albedo, and indices related to rock chemical components.In addition, the geographical environment, especially land surface temperature and atmospheric moisture content, may influence the surface morphology of rocks [48], such as changed surface structural characteristics, which may result from sedimentation occurring within weathered fractures.The geographic environment in a specific study area may limit the robustness of this strategy, which would also limit the accuracy of image classification.Hence, for other regions or other rock types, more analysis and validation is necessary.

Conclusions
Prior studies have demonstrated the capability of integrating textural features with spectral features for image classification and pattern recognition.However, these studies focus mostly on the improvement or design of texture-derived methods.In addition, the used textural features are calculated from a single remote sensing image, resulting in invalid results, which has been demonstrated in Chen et al. [69].In this paper, we focused on examining the sensitivity of different texture images to aid spectral features for lithological classification.So, the J-M distance was used to examine the performance of textural vectors derived from different sources and to select optimal combinations of textural and spectral vectors for lithology identification.The statistical results indicate that more textural vectors yield higher class separability.Then, multiple textural vectors and multiple spectral vectors were integrated using the SVM model to improve lithological classification.Classification results were evaluated based on the confusion matrix.Compared with conventional classification based only on spectral features, the classification accuracy obtained by integrated textural and spectral features is noticeably improved, especially for granite and quartzose rocks, which show an increase in classification accuracy of 38.84% and 7.03%, respectively, indicating that textural features provide useful supplementary information for lithological identification.We conclude that (i) textural features that reveal rock differences in surface morphology have a remarkable ability to improve lithological classification, especially for rocks that have subtle spectral features; (ii) texture images derived from different data sources have different sensitivities for lithological identification.So, the use of multiple textural vectors has a better performance than that of a single textural vector to aid spectral information for lithological classification; (iii) it is necessary to select optimal feature vector combinations using appropriate methods, such as the J-M distance.Furthermore, more useful indicators, similar to the textural features derived from remote sensing imagery, need to be explored and integrated with spectral features using suitable classifiers to improve the accuracy of lithological classification.Nevertheless, we propose a referable technique to improve the classification of silicate rocks by fusing textural features with spectral features, which will aid in lithological mapping at macroscopic scales and guide further geological exploration in harsh natural environments.

Figure 1 .
Figure 1.Location of study area.

Figure 1 .
Figure 1.Location of study area.

Figure 2 .
Figure 2. (a) Generalized lithological map of the study area, showing the major lithology boundaries, modified after Ding et al. [49], Copyright 2014, Elsevier; and (b) false color composition with R = band 5 (near-infrared (NIR), 0.845-0.885),G = band 4 (Red, 0.630-0.680),and B = band 3 (Green, 0.525-0.600) in the study area with the lithological boundaries of gabbro, serpentinite, granite and Lower Ordovician Sandstone-Slate Group derived from Figure 2a to highlight the distribution of the discussed rock types in this paper.

Figure 2 .
Figure 2. (a) Generalized lithological map of the study area, showing the major lithology boundaries, modified after Ding et al. [49], Copyright 2014, Elsevier; and (b) false color composition with R = band 5 (near-infrared (NIR), 0.845-0.885),G = band 4 (Red, 0.630-0.680),and B = band 3 (Green, 0.525-0.600) in the study area with the lithological boundaries of gabbro, serpentinite, granite and Lower Ordovician Sandstone-Slate Group derived from Figure 2 (a) to highlight the distribution of the discussed rock types in this paper.

Figure 3 .
Figure 3. (a) Vegetation pixels in green with 0.4 < normalized difference vegetation index (NDVI) < 1.0; and (b) cloud pixels in white with 0.35 < reflectance of NIR < 1.0 in the study area.

4. 1 .
Spatial Textural Feature Analysis 4.1.1.Spatial Textural Feature Extraction Seven texture images were derived from seven spectral bands of Landsat 8 data using the box-

Figure 3 .
Figure 3. (a) Vegetation pixels in green with 0.4 < normalized difference vegetation index (NDVI) < 1.0; and (b) cloud pixels in white with 0.35 < reflectance of NIR < 1.0 in the study area.

Figure 4 .
Figure 4. Mean fractal dimension ( ) value for the four rock types in seven texture images ( represents textural feature image derived from spectral band ).

Figure 4 .
Figure 4. Mean fractal dimension (FD) value for the four rock types in seven texture images (TFi represents textural feature image derived from spectral band i).

Figure 5 .
Figure 5. J-M distance of pairwise rocks in different combinations of texture and spectral vectors.Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock.The labels in (a) also apply to (b-f).Different colors in this figure means the different J-M distance.A darker shades of color means a larger J-M distance.

Figure 5 .
Figure 5. J-M distance of pairwise rocks in different combinations of texture and spectral vectors.Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock.The labels in (a) also apply to (b-f).Different colors in this figure means the different J-M distance.A darker shades of color means a larger J-M distance.

Figure 6 .
Figure 6.Increases in class separability for seven series compared to results obtained using only spectral vectors.Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock.

Figure 6 .
Figure 6.Increases in class separability for seven series compared to results obtained using only spectral vectors.Ga is gabbro, Se is serpentinite, Gr is granite, and Qu is quartzose rock.

Figure 7 .
Figure 7. Landsat 8 classification results of the study area using (a) support vector machine (SVM) with only seven spectral vectors and no textural vector (the overall accuracy is 74.69% and κ is 0.64); and (b) SVM with seven textural vectors fused with seven spectral vectors (overall accuracy is 83.73% and κ is 0.77).

Figure 8 .
Figure 8. Zoom-in and comparison of classification results of (a) SVM with seven spectral vectors and no textural feature; and (b) SVM with seven spectral vectors fused with seven textural vectors for gabbro, quartzose rock, and granite.The ellipses are used to highlight the changes between Figure 8 (a) and Figure (b).

Figure 7 .
Figure 7. Landsat 8 classification results of the study area using (a) support vector machine (SVM) with only seven spectral vectors and no textural vector (the overall accuracy is 74.69% and κ is 0.64); and (b) SVM with seven textural vectors fused with seven spectral vectors (overall accuracy is 83.73% and κ is 0.77).

Figure 7 .
Figure 7. Landsat 8 classification results of the study area using (a) support vector machine (SVM) with only seven spectral vectors and no textural vector (the overall accuracy is 74.69% and κ is 0.64); and (b) SVM with seven textural vectors fused with seven spectral vectors (overall accuracy is 83.73% and κ is 0.77).

Figure 8 .
Figure 8. Zoom-in and comparison of classification results of (a) SVM with seven spectral vectors and no textural feature; and (b) SVM with seven spectral vectors fused with seven textural vectors for gabbro, quartzose rock, and granite.The ellipses are used to highlight the changes between Figure 8 (a) and Figure (b).

Figure 8 .
Figure 8. Zoom-in and comparison of classification results of (a) SVM with seven spectral vectors and no textural feature; and (b) SVM with seven spectral vectors fused with seven textural vectors for gabbro, quartzose rock, and granite.The ellipses are used to highlight the changes between Figure 8 (a) and (b).

Table 2 .
Different combinations of spectral and textural vectors a .
The same convention applies to the rest of the table contents.

Table 4 .
Confusion matrix and accuracy evaluation of classification results using support vector machine (SVM) with seven spectral vectors.

Table 5 .
Confusion matrix and accuracy evaluation of classification results using SVM with seven spectral vectors fused with seven textural vectors.