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Special Issue "Recent Advances in Hyperspectral Image Processing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 19602

Special Issue Editors

Prof. Dr. Liangpei Zhang
grade E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: pattern analysis and machine learning; image processing engineering; application of remote sensing; computational intelligence and its application in remote sensing image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Shi
E-Mail Website
Guest Editor
Associate Professor, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Interests: intelligent mining and analysis of remote sensing big data
Special Issues, Collections and Topics in MDPI journals
Dr. Yanni Dong
E-Mail Website
Guest Editor
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: hyperspectral target detection; dimensionality reduction; scene classification; metric learning; transfer learning; multi-source remote sensing data geological interpretation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past decades, hyperspectral imagery (HSI) has helped to observe and analyze various ground cover materials with abundant spectral information from hundreds or thousands of spectral bands. The rich spectral information provided by HSI makes it possible to distinguish various surface materials because every material has its own reflectance spectra characteristics, thus allowing for the application of HSI in many fields, including agriculture, forestry, environmental monitoring, geology, mineralogy, military, and medical imaging. Hyperspectral image processing techniques are developing rapidly in the current remote sensing community. Particularly, the development of computer technology and calculation techniques such as artificial intelligence, deep learning, and weakly supervised learning has expanded and enhanced the application direction and scope of hyperspectral image processing in recent years. However, several challenges and open problems are still waiting for efficient solutions and novel methodologies. The main goal of this Special Issue is to address advanced topics related to hyperspectral image processing.

This Special Issue is open to any researchers working on hyperspectral data applications and processing. Topics of interests include but are not limited to the following:

  • Radiative transfer modeling;
  • Fusion and resolution enhancement;
  • Spectral signature libraries and databases;
  • Denoising, restoration, and super resolution;
  • Endmember extraction and unmixing;
  • Dimensionality reduction and band selection;
  • Classification and segmentation;
  • Target and anomaly detection;
  • Change detection and time-series HSI analysis;
  • Artificial intelligence for HSI;
  • Mineral mapping, lithologic mapping, and geological applications;
  • Water quality monitoring;
  • Soil environment monitoring;
  • Forest species identification;
  • Wetland classification;
  • Vegetation health monitoring.

Prof. Dr. Liangpei Zhang
Dr. Lefei Zhang
Dr. Qian Shi
Dr. Yanni Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Hyperspectral remote sensing
  • Image processing
  • Machine learning
  • Pattern recognition

Published Papers (12 papers)

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Research

Article
A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification
Remote Sens. 2022, 14(13), 3091; https://doi.org/10.3390/rs14133091 - 27 Jun 2022
Cited by 1 | Viewed by 897
Abstract
To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head [...] Read more.
To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower computation costs. To effectively capture rich contextual dependencies from both domains, we decouple the spatial and spectral contextual attention into two sub-blocks, SaMCA and SeMCA, where depth-wise convolution is employed to contextualize the input keys in the pure dimension. Thereafter, multi-head local attentions are implemented as group processing when the keys are alternately concatenated with the queries. In particular, in the SeMCA block, we group the spatial pixels by evenly sampling and create multi-head channel attention on each sampling set, to reduce the number of the training parameters and avoid the storage increase. In addition, the static contextual keys are fused with the dynamic attentional features in each block to strengthen the capacity of the model in data representation. Finally, the decoupled sub-blocks are weighted and summed together for 3-D attention perception of HSI. The DMuCA module is then plugged into a ResNet to perform HSI classification. Extensive experiments demonstrate that our proposed DMuCA achieves excellent results over several state-of-the-art attention mechanisms with the same backbone. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery
Remote Sens. 2022, 14(11), 2523; https://doi.org/10.3390/rs14112523 - 25 May 2022
Cited by 2 | Viewed by 976
Abstract
Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resulting CD map. The spectral unmixing (SU) [...] Read more.
Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resulting CD map. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of land cover. Subsequently, the CD map is created by comparing the abundance images. However, based only on the abundance images created through the SU method, they are unable to effectively provide detailed change information. Meanwhile, the features of change information cannot be sufficiently extracted by the traditional sub-pixel CD framework, which leads to a poor CD result. To address these problems, this paper presents an integrated CD method based on multi-endmember spectral unmixing, joint matrix and CNN (MSUJMC) for HSI. Three main steps are considered to accomplish this task. First, considering the endmember spectral variability, more reliable endmember abundance information is obtained by multi-endmember spectral unmixing (MSU). Second, the original image features are incorporated with the abundance images using a joint matrix (JM) algorithm to provide more temporal and spatial land cover change information characteristics. Third, to efficiently extract the change features and to better handle the fused multi-source information, the convolutional neural network (CNN) is introduced to realize a high-accuracy CD result. The proposed method has been verified on simulated and real multitemporal HSI datasets, which provide multiple changes. Experimental results verify the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops
Remote Sens. 2021, 13(23), 4735; https://doi.org/10.3390/rs13234735 - 23 Nov 2021
Cited by 1 | Viewed by 1653
Abstract
The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease [...] Read more.
The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Hyperspectral Image Classification Based on Two-Branch Spectral–Spatial-Feature Attention Network
Remote Sens. 2021, 13(21), 4262; https://doi.org/10.3390/rs13214262 - 23 Oct 2021
Cited by 3 | Viewed by 1059
Abstract
Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is [...] Read more.
Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Deep Ensembles for Hyperspectral Image Data Classification and Unmixing
Remote Sens. 2021, 13(20), 4133; https://doi.org/10.3390/rs13204133 - 15 Oct 2021
Cited by 5 | Viewed by 1202
Abstract
Hyperspectral images capture very detailed information about scanned objects and, hence, can be used to uncover various characteristics of the materials present in the analyzed scene. However, such image data are difficult to transfer due to their large volume, and generating new ground-truth [...] Read more.
Hyperspectral images capture very detailed information about scanned objects and, hence, can be used to uncover various characteristics of the materials present in the analyzed scene. However, such image data are difficult to transfer due to their large volume, and generating new ground-truth datasets that could be utilized to train supervised learners is costly, time-consuming, very user-dependent, and often infeasible in practice. The research efforts have been focusing on developing algorithms for hyperspectral data classification and unmixing, which are two main tasks in the analysis chain of such imagery. Although in both of them, the deep learning techniques have bloomed as an extremely effective tool, designing the deep models that generalize well over the unseen data is a serious practical challenge in emerging applications. In this paper, we introduce the deep ensembles benefiting from different architectural advances of convolutional base models and suggest a new approach towards aggregating the outputs of base learners using a supervised fuser. Furthermore, we propose a model augmentation technique that allows us to synthesize new deep networks based on the original one by injecting Gaussian noise into the model’s weights. The experiments, performed for both hyperspectral data classification and unmixing, show that our deep ensembles outperform base spectral and spectral-spatial deep models and classical ensembles employing voting and averaging as a fusing scheme in both hyperspectral image analysis tasks. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
Remote Sens. 2021, 13(19), 3941; https://doi.org/10.3390/rs13193941 - 01 Oct 2021
Cited by 2 | Viewed by 1000
Abstract
With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral [...] Read more.
With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification
Remote Sens. 2021, 13(16), 3306; https://doi.org/10.3390/rs13163306 - 20 Aug 2021
Cited by 4 | Viewed by 1173
Abstract
With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, [...] Read more.
With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, owing to the noise of observation systems and the ever-changing external observation environments, the observed asteroid spectral data always contain noise and outliers exhibiting indivisible pattern characteristics, which will bring great challenges to the precise classification of asteroids. In order to alleviate the problem and to improve the separability and classification accuracy for different kinds of asteroids, this paper presents a novel Neighboring Discriminant Component Analysis (NDCA) model for asteroid spectrum feature learning. The key motivation is to transform the asteroid spectral data from the observation space into a feature subspace wherein the negative effects of outliers and noise will be minimized while the key category-related valuable knowledge in asteroid spectral data can be well explored. The effectiveness of the proposed NDCA model is verified on real-world asteroid reflectance spectra measured over the wavelength range from 0.45 to 2.45 μm, and promising classification performance has been achieved by the NDCA model in combination with different classifier models, such as the nearest neighbor (NN), support vector machine (SVM) and extreme learning machine (ELM). Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner
Remote Sens. 2021, 13(16), 3226; https://doi.org/10.3390/rs13163226 - 13 Aug 2021
Cited by 2 | Viewed by 1725
Abstract
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In [...] Read more.
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends on the size and quality of training dataset, constraining the application of deep learning under the situation where training dataset is not available or of low quality. In this paper, we introduce a novel fusion method, which operates in a self-supervised manner, to the task of hyperspectral and multispectral image fusion without training datasets. Our method proposes two constraints constructed by low-resolution hyperspectral images and fake high-resolution hyperspectral images obtained from a simple diffusion method. Several simulation and real-data experiments are conducted with several popular remote sensing hyperspectral data under the condition where training datasets are unavailable. Quantitative and qualitative results indicate that the proposed method outperforms those traditional methods by a large extent. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
A Novel Change Detection Approach Based on Spectral Unmixing from Stacked Multitemporal Remote Sensing Images with a Variability of Endmembers
Remote Sens. 2021, 13(13), 2550; https://doi.org/10.3390/rs13132550 - 29 Jun 2021
Cited by 4 | Viewed by 1097
Abstract
Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the [...] Read more.
Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the resulting thematic map challenging. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of the land covers. However, there are accumulated errors in the fractional difference images, which lead to a poor change detection results. Meanwhile, the spectra variation of the endmember and the heterogeneity of the land cover materials cannot be fully considered in the traditional framework. In order to solve this problem, a novel change detection approach with image stacking and dividing based on spectral unmixing while considering the variability of endmembers (CD_SDSUVE) was proposed in this paper. Firstly, the remote sensing images at different times were stacked into a unified framework. After that, several patch images were produced by dividing the stacked images so that the similar endmembers according to each land cover can be completely extracted and compared. Finally, the multiple endmember spectral mixture analysis (MESMA) is performed, and the abundant images were combined to produce the entire change detection thematic map. This proposed algorithm was implemented and compared to four relevant state-of-the-art methods on three experimental data, whereby the results confirmed that it effectively improved the accuracy. In the simulated data, the overall accuracy (OA) and Kappa coefficient values were 99.61% and 0.99. In the two real data, the maximum of OA were acquired with 93.26% and 80.85%, which gained 14.88% and 13.42% over the worst results at most. Meanwhile, the Kappa coefficient value was consistent with the OA. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection
Remote Sens. 2021, 13(13), 2536; https://doi.org/10.3390/rs13132536 - 29 Jun 2021
Cited by 20 | Viewed by 2659
Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment [...] Read more.
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding
Remote Sens. 2021, 13(7), 1363; https://doi.org/10.3390/rs13071363 - 02 Apr 2021
Cited by 23 | Viewed by 2215
Abstract
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In [...] Read more.
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Automatic Cotton Mapping Using Time Series of Sentinel-2 Images
Remote Sens. 2021, 13(7), 1355; https://doi.org/10.3390/rs13071355 - 01 Apr 2021
Cited by 6 | Viewed by 1753
Abstract
Large-scale crop mapping is essential for agricultural management. Phenological variation often exists in the same crop due to different climatic regions or practice management, resulting in current classification models requiring sufficient training samples from different regions. However, the cost of sample collection is [...] Read more.
Large-scale crop mapping is essential for agricultural management. Phenological variation often exists in the same crop due to different climatic regions or practice management, resulting in current classification models requiring sufficient training samples from different regions. However, the cost of sample collection is more time-consuming, costly, and labor-intensive, so it is necessary to develop automatic crop mapping models that require only a few samples and can be extended to a large area. In this study, a new white bolls index (WBI) based on the unique canopy of cotton at the bolls opening stage was proposed, which can characterize the intensity of bolls opening. The value of WBI will increase as the opening of the bolls increases. As a result, the white bolls index can be used to detect cotton automatically from other crops. Four study areas in different regions were used to evaluate the WBI performance. The overall accuracy (OA) for the four study sites was more than 82%. Additionally, the dates when the opening stage of bolls begins can be determined based on the time series of WBI. The results of this research demonstrated the potential of the proposed approach for cotton mapping using sentinel-2 time series of remotely sensed data. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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