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Advances in Hyperspectral Remote Sensing 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: 31 July 2024 | Viewed by 20475

Special Issue Editors

School of Robotics, Hunan University, Changsha 410082, China
Interests: remote sensing image processing and application
1. Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, 09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Vienna, Austria
Interests: machine (deep) learning; image and signal processing; multisensor data fusion
Special Issues, Collections and Topics in MDPI journals
School of Computer Science, Fudan University, Shanghai 200438, China
Interests: big data; data science; industrial intelligence; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Hyperspectral remote sensing images have become a well established data source for precision agriculture, resource investigation, environmental monitoring, national defense, and other important fields for their fine spectral information. Meanwhile, the renewal of space industry in various countries has also established a relatively complete remote sensing data acquisition system, which provides data support and new power for hyperspectral remote sensing earth observation. Under the background of rapid development of artificial intelligence, big data, 5G, and other advanced technologies, the processing and reasonable application of massive hyperspectral data has become a hot research topic in the field of remote sensing.

This Special Issue aims to study the methods and applications of hyperspectral remote sensing image processing. Topics may range from comparative study, overview, different types of hyperspectral remote sensing image processing methods to more comprehensive practical applications. Therefore, the method design, performance evaluation, application implementation, and other issues based on hyperspectral remote sensing image processing are welcome. Articles may address, but are not limited to, the following topics:

  • Comparative study or review of hyperspectral image processing methods;
  • Primary processing methods of hyperspectral remote sensing images (e.g., rectification, denoising, restoration, enhancement);
  • Intermediate processing methods of hyperspectral remote sensing images (e.g., feature selection and extraction, super-resolution, clustering, image fusion, unmixing);
  • Advanced processing methods of hyperspectral remote sensing images (e.g., classification, target detection, anomaly detection, segmentation, scene recognition, image interpretation);
  • Application of hyperspectral remote sensing image processing results in real scenarios (e.g., disaster monitoring, precision agriculture, resource investigation);
  • Lightweight networks and efficient targeted systems designed for hyperspectral remote sensing image processing;
  • Opportunities and challenges in hyperspectral remote sensing image processing.

Prof. Dr. Xudong Kang
Prof. Dr. Pedram Ghamisi
Dr. Mingmin Chi
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 2700 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

  • remote sensing
  • hyperspectral image
  • feature extraction
  • image processing
  • image analysis
  • artificial intelligence
  • image classification
  • information fusion

Published Papers (14 papers)

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Research

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20 pages, 1544 KiB  
Article
Hyperspectral Image Classification Using Spectral–Spatial Double-Branch Attention Mechanism
Remote Sens. 2024, 16(1), 193; https://doi.org/10.3390/rs16010193 - 02 Jan 2024
Viewed by 834
Abstract
In recent years, deep learning methods utilizing convolutional neural networks have been extensively employed in hyperspectral image classification (HSI) applications. Nevertheless, while a substantial number of stacked 3D convolutions can indeed achieve high classification accuracy, they also introduce a significant number of parameters [...] Read more.
In recent years, deep learning methods utilizing convolutional neural networks have been extensively employed in hyperspectral image classification (HSI) applications. Nevertheless, while a substantial number of stacked 3D convolutions can indeed achieve high classification accuracy, they also introduce a significant number of parameters to the model, resulting in inefficiency. Furthermore, such intricate models often exhibit limited classification accuracy when confronted with restricted sample data, i.e., small sample problems. Therefore, we propose a spectral–spatial double-branch network (SSDBN) with an attention mechanism for HSI classification. The SSDBN is designed with two independent branches to extract spectral and spatial features, respectively, incorporating multi-scale 2D convolution modules, long short-term memory (LSTM), and an attention mechanism. The flexible use of 2D convolution, instead of 3D convolution, significantly reduces the model’s parameter count, while the effective spectral–spatial double-branch feature extraction method allows SSDBN to perform exceptionally well in handling small sample problems. When tested on 5%, 0.5%, and 5% of the Indian Pines, Pavia University, and Kennedy Space Center datasets, SSDBN achieved classification accuracies of 97.56%, 96.85%, and 98.68%, respectively. Additionally, we conducted a comparison of training and testing times, with results demonstrating the remarkable efficiency of SSDBN. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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30 pages, 6603 KiB  
Article
Vision Transformer-Based Ensemble Learning for Hyperspectral Image Classification
Remote Sens. 2023, 15(21), 5208; https://doi.org/10.3390/rs15215208 - 02 Nov 2023
Viewed by 1054
Abstract
Hyperspectral image (HSI) classification, due to its characteristic combination of images and spectra, has important applications in various fields through pixel-level image classification. The fusion of spatial–spectral features is a topic of great interest in the context of hyperspectral image classification, which typically [...] Read more.
Hyperspectral image (HSI) classification, due to its characteristic combination of images and spectra, has important applications in various fields through pixel-level image classification. The fusion of spatial–spectral features is a topic of great interest in the context of hyperspectral image classification, which typically requires selecting a larger spatial neighborhood window, potentially leading to overlaps between training and testing samples. Vision Transformer (ViTs), with their powerful global modeling abilities, have had a significant impact in the field of computer vision through various variants. In this study, an ensemble learning framework for HSI classification is proposed by integrating multiple variants of ViTs, achieving high-precision pixel-level classification. Firstly, the spatial shuffle operation was introduced to preprocess the training samples for HSI classification. By randomly shuffling operations using smaller spatial neighborhood windows, a greater potential spatial distribution of pixels can be described. Then, the training samples were transformed from a 3D cube to a 2D image, and a learning framework was built by integrating seven ViT variants. Finally, a two-level ensemble strategy was employed to achieve pixel-level classification based on the results of multiple ViT variants. Our experimental results demonstrate that the proposed ensemble learning framework achieves stable and significantly high classification accuracy on multiple publicly available HSI datasets. The proposed method also shows notable classification performance with varying numbers of training samples. Moreover, herein, it is proven that the spatial shuffle operation plays a crucial role in improving classification accuracy. By introducing superior individual classifiers, the proposed ensemble framework is expected to achieve even better classification performance. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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25 pages, 12707 KiB  
Article
Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation
Remote Sens. 2023, 15(20), 5028; https://doi.org/10.3390/rs15205028 - 19 Oct 2023
Viewed by 671
Abstract
In hyperspectral unmixing, dealing with nonlinear mixing effects and spectral variability (SV) is a significant challenge. Traditional linear unmixing can be seriously deteriorated by the coupled residuals of nonlinearity and SV in remote sensing scenarios. For the simplification of calculation, current unmixing studies [...] Read more.
In hyperspectral unmixing, dealing with nonlinear mixing effects and spectral variability (SV) is a significant challenge. Traditional linear unmixing can be seriously deteriorated by the coupled residuals of nonlinearity and SV in remote sensing scenarios. For the simplification of calculation, current unmixing studies usually separate the consideration of nonlinearity and SV. As a result, errors individually caused by the nonlinearity or SV still persist, potentially leading to overfitting and the decreased accuracy of estimated endmembers and abundances. In this paper, a novel unsupervised nonlinear unmixing method accounting for SV is proposed. First, an improved Fisher transformation scheme is constructed by combining an abundance-driven dynamic classification strategy with superpixel segmentation. It can enlarge the differences between different types of pixels and reduce the differences between pixels corresponding to the same class, thereby reducing the influence of SV. Besides, spectral similarity can be well maintained in local homogeneous regions. Second, the polynomial postnonlinear model is employed to represent observed pixels and explain nonlinear components. Regularized by a Fisher transformation operator and abundances’ spatial smoothness, data reconstruction errors in the original spectral space and the transformed space are weighed to derive the unmixing problem. Finally, this problem is solved by a dimensional division-based particle swarm optimization algorithm to produce accurate unmixing results. Extensive experiments on synthetic and real hyperspectral remote sensing data demonstrate the superiority of the proposed method in comparison with state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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18 pages, 2509 KiB  
Article
Hyperspectral Image Classification via Spatial Shuffle-Based Convolutional Neural Network
Remote Sens. 2023, 15(16), 3960; https://doi.org/10.3390/rs15163960 - 10 Aug 2023
Cited by 1 | Viewed by 791
Abstract
The unique spatial–spectral integration characteristics of hyperspectral imagery (HSI) make it widely applicable in many fields. The spatial–spectral feature fusion-based HSI classification has always been a research hotspot. Typically, classification methods based on spatial–spectral features will select larger neighborhood windows to extract more [...] Read more.
The unique spatial–spectral integration characteristics of hyperspectral imagery (HSI) make it widely applicable in many fields. The spatial–spectral feature fusion-based HSI classification has always been a research hotspot. Typically, classification methods based on spatial–spectral features will select larger neighborhood windows to extract more spatial features for classification. However, this approach can also lead to the problem of non-independent training and testing sets to a certain extent. This paper proposes a spatial shuffle strategy that selects a smaller neighborhood window and randomly shuffles the pixels within the window. This strategy simulates the potential patterns of the pixel distribution in the real world as much as possible. Then, the samples of a three-dimensional HSI cube is transformed into two-dimensional images. Training with a simple CNN model that is not optimized for architecture can still achieve very high classification accuracy, indicating that the proposed method of this paper has considerable performance-improvement potential. The experimental results also indicate that the smaller neighborhood windows can achieve the same, or even better, classification performance compared to larger neighborhood windows. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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24 pages, 7941 KiB  
Article
Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution
Remote Sens. 2023, 15(12), 3066; https://doi.org/10.3390/rs15123066 - 12 Jun 2023
Viewed by 900
Abstract
Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation [...] Read more.
Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation complexity. To solve the above problems, a novel method based on a channel multilayer perceptron (CMLP) is presented in this article, which aims to obtain a better performance while reducing the computational cost. To sufficiently extract spectral features, a local-global spectral integration block is proposed, which consists of CMLP and some parameter-free operations. The block can extract local and global spectral features with low computational cost. In addition, a spatial feature group extraction block based on the CycleMLP framework is designed; it can extract local spatial features well and reduce the computation complexity and number of parameters. Extensive experiments demonstrate that our method achieves a good performance compared with other methods. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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21 pages, 13988 KiB  
Article
Rethinking 3D-CNN in Hyperspectral Image Super-Resolution
Remote Sens. 2023, 15(10), 2574; https://doi.org/10.3390/rs15102574 - 15 May 2023
Viewed by 1009
Abstract
Recently, CNN-based methods for hyperspectral image super-resolution (HSISR) have achieved outstanding performance. Due to the multi-band property of hyperspectral images, 3D convolutions are natural candidates for extracting spatial–spectral correlations. However, pure 3D CNN models are rare to see, since they are generally considered [...] Read more.
Recently, CNN-based methods for hyperspectral image super-resolution (HSISR) have achieved outstanding performance. Due to the multi-band property of hyperspectral images, 3D convolutions are natural candidates for extracting spatial–spectral correlations. However, pure 3D CNN models are rare to see, since they are generally considered to be too complex, require large amounts of data to train, and run the risk of overfitting on relatively small-scale hyperspectral datasets. In this paper, we question this common notion and propose Full 3D U-Net (F3DUN), a full 3D CNN model combined with the U-Net architecture. By introducing skip connections, the model becomes deeper and utilizes multi-scale features. Extensive experiments show that F3DUN can achieve state-of-the-art performance on HSISR tasks, indicating the effectiveness of the full 3D CNN on HSISR tasks, thanks to the carefully designed architecture. To further explore the properties of the full 3D CNN model, we develop a 3D/2D mixed model, a popular kind of model prior, called Mixed U-Net (MUN) which shares a similar architecture with F3DUN. Through analysis on F3DUN and MUN, we find that 3D convolutions give the model a larger capacity; that is, the full 3D CNN model can obtain better results than the 3D/2D mixed model with the same number of parameters when it is sufficiently trained. Moreover, experimental results show that the full 3D CNN model could achieve competitive results with the 3D/2D mixed model on a small-scale dataset, suggesting that 3D CNN is less sensitive to data scaling than what people used to believe. Extensive experiments on two benchmark datasets, CAVE and Harvard, demonstrate that our proposed F3DUN exceeds state-of-the-art HSISR methods both quantitatively and qualitatively. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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13 pages, 2269 KiB  
Communication
High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset
Remote Sens. 2023, 15(5), 1433; https://doi.org/10.3390/rs15051433 - 03 Mar 2023
Cited by 4 | Viewed by 1890
Abstract
The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote [...] Read more.
The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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20 pages, 21995 KiB  
Article
Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms
Remote Sens. 2023, 15(4), 1050; https://doi.org/10.3390/rs15041050 - 15 Feb 2023
Viewed by 1197
Abstract
Anomaly detection is hampered by band redundancy and the restricted reconstruction ability of spectral–spatial information in hyperspectral remote sensing. A novel hyperspectral anomaly detection method integrating differential attribute profiles and genetic algorithms (DAPGA) is proposed to sufficiently extract the spectral–spatial features and automatically [...] Read more.
Anomaly detection is hampered by band redundancy and the restricted reconstruction ability of spectral–spatial information in hyperspectral remote sensing. A novel hyperspectral anomaly detection method integrating differential attribute profiles and genetic algorithms (DAPGA) is proposed to sufficiently extract the spectral–spatial features and automatically optimize the selection of the optimal features. First, a band selection method with cross-subspace combination is employed to decrease the spectral dimension and choose representative bands with rich information and weak correlation. Then, the differentials of attribute profiles are calculated by four attribute types and various filter parameters for multi-scale and multi-type spectral–spatial feature decomposition. Finally, the ideal discriminative characteristics are reserved and incorporated with genetic algorithms to cluster each differential attribute profile by dissimilarity assessment. Experiments run on a variety of genuine hyperspectral datasets including airport, beach, urban, and park scenes show that the effectiveness of the proposed algorithm has great improvement with existing state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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20 pages, 4118 KiB  
Article
Double Deep Q-Network for Hyperspectral Image Band Selection in Land Cover Classification Applications
Remote Sens. 2023, 15(3), 682; https://doi.org/10.3390/rs15030682 - 23 Jan 2023
Cited by 6 | Viewed by 2174
Abstract
Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications. However, highly correlated spectral bands in hyperspectral data lead to computational complexity, which limits many applications or [...] Read more.
Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications. However, highly correlated spectral bands in hyperspectral data lead to computational complexity, which limits many applications or traditional methods when applied to hyperspectral data. The dimensionality reduction of hyperspectral data becomes one of the most important pre-processing steps in hyperspectral data analysis. Recently, deep reinforcement learning (DRL) has been introduced to hyperspectral data band selection (BS); however, the current DRL methods for hyperspectral data BS simply remove redundant bands, lack the significance analysis for the selected bands, and the reward mechanisms used in DRL only take basic forms in general. In this paper, a new reward mechanism strategy has been proposed, and Double Deep Q-Network (DDQN) is introduced during BS using DRL to improve the network stabilities and avoid local optimum. To verify the effect of the proposed BS method, land cover classification experiments were designed and carried out to analyze and compare the proposed method with other BS methods. In the land cover classification experiments, the overall accuracy (OA) of the proposed method can reach 98.37%, the average accuracy (AA) is 95.63%, the kappa coefficient (Kappa) is 97.87%. Overall, the proposed method is superior to other BS methods. Experiments have also shown that the proposed method works not only for airborne hyperspectral data (AVIRIS and HYDICE), but also for hyperspectral satellite data, such as PRISMA data. When hyperspectral data is applied to similar applications, the proposed BS method could be a candidate for the BS preprocessing options. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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22 pages, 19711 KiB  
Article
Parallel Spectral–Spatial Attention Network with Feature Redistribution Loss for Hyperspectral Change Detection
Remote Sens. 2023, 15(1), 246; https://doi.org/10.3390/rs15010246 - 31 Dec 2022
Cited by 5 | Viewed by 1489
Abstract
Change detection methods using hyperspectral remote sensing can precisely identify differences of the same area at different observing times. However, due to massive spectral bands, current change detection methods are vulnerable to unrelatedspectral and spatial information in hyperspectral images with the stagewise calculation [...] Read more.
Change detection methods using hyperspectral remote sensing can precisely identify differences of the same area at different observing times. However, due to massive spectral bands, current change detection methods are vulnerable to unrelatedspectral and spatial information in hyperspectral images with the stagewise calculation of attention maps. Besides, current change methods arrange hidden change features in a random distribution form, which cannot express a class-oriented discrimination in advance. Moreover, existent deep change methods have not fully considered the hierarchical features’ reuse and the fusion of the encoder–decoder framework. To better handle the mentioned existent problems, the parallel spectral–spatial attention network with feature redistribution loss (TFR-PS2ANet) is proposed. The contributions of this article are summarized as follows: (1) a parallel spectral–spatial attention module (PS2A) is introduced to enhance relevant information and suppress irrelevant information in parallel using spectral and spatial attention maps extracted from the original hyperspectral image patches; (2) the feature redistribution loss function (FRL) is introduced to construct the class-oriented feature distribution, which organizes the change features in advance and improves the discriminative abilities; (3) a two-branch encoder–decoder framework is developed to optimize the hierarchical transfer and change features’ fusion; Extensive experiments were carried out on several real datasets. The results show that the proposed PS2A can enhance significant information effectively and the FRL can optimize the class-oriented feature distribution. The proposed method outperforms most existent change detection methods. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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25 pages, 55195 KiB  
Article
The Influence of Image Degradation on Hyperspectral Image Classification
Remote Sens. 2022, 14(20), 5199; https://doi.org/10.3390/rs14205199 - 17 Oct 2022
Cited by 3 | Viewed by 1869
Abstract
Recent advances in hyperspectral remote sensing techniques, especially in the hyperspectral image classification techniques, have provided efficient support for recognizing and analyzing ground objects. To date, most of the existing classification techniques have been designed for ideal hyperspectral images and have verified their [...] Read more.
Recent advances in hyperspectral remote sensing techniques, especially in the hyperspectral image classification techniques, have provided efficient support for recognizing and analyzing ground objects. To date, most of the existing classification techniques have been designed for ideal hyperspectral images and have verified their effectiveness on high-quality hyperspectral image datasets. However, in real applications, available hyperspectral images often contain varying degrees of image degradation. Whether or not the classification accuracy will be reduced due to degradation problems in input data, and how it will be reduced become interesting questions. In this paper, we explore the effects of degraded inputs in hyperspectral image classification including the five typical degradation problems of low spatial resolution, Gaussian noise, stripe noise, fog, and shadow. Seven representative classification methods are chosen from different categories of classification methods and applied to analyze the specific influences of image degradation problems. Experiments are carried out from the aspects of single-type synthetic image degradation and mixed-type real image degradation. Consistent results from synthetic and real-data experiments show that the effects of degraded hyperspectral data in classification are related to image features, degradation types, degradation degrees, and the characteristics of classification methods. This provides constructive information for method selection in real applications where high-quality hyperspectral data are difficult to obtain and encourages researchers to develop more stable and effective classification methods for degraded hyperspectral images. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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21 pages, 2957 KiB  
Article
A CNN Ensemble Based on a Spectral Feature Refining Module for Hyperspectral Image Classification
Remote Sens. 2022, 14(19), 4982; https://doi.org/10.3390/rs14194982 - 07 Oct 2022
Cited by 2 | Viewed by 1331
Abstract
In the study of hyperspectral image classification based on machine learning theory and techniques, the problems related to the high dimensionality of the images and the scarcity of training samples are widely discussed as two main issues that limit the performance of the [...] Read more.
In the study of hyperspectral image classification based on machine learning theory and techniques, the problems related to the high dimensionality of the images and the scarcity of training samples are widely discussed as two main issues that limit the performance of the data-driven classifiers. These two issues are closely interrelated, but are usually addressed separately. In our study, we try to kill two birds with one stone by constructing an ensemble of lightweight base models embedded with spectral feature refining modules. The spectral feature refining module is a technique based on the mechanism of channel attention. This technique can not only perform dimensionality reduction, but also provide diversity within the ensemble. The proposed ensemble can provide state-of-the-art performance when the training samples are quite limited. Specifically, using only a total of 200 samples from each of the four popular benchmark data sets (Indian Pines, Salinas, Pavia University and Kennedy Space Center), we achieved overall accuracies of 89.34%, 95.75%, 93.58%, and 98.14%, respectively. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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Review

Jump to: Research

34 pages, 33895 KiB  
Review
Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey
Remote Sens. 2023, 15(15), 3793; https://doi.org/10.3390/rs15153793 - 30 Jul 2023
Cited by 1 | Viewed by 1058
Abstract
In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and [...] Read more.
In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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43 pages, 3374 KiB  
Review
From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images
Remote Sens. 2023, 15(11), 2832; https://doi.org/10.3390/rs15112832 - 29 May 2023
Viewed by 1775
Abstract
Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil [...] Read more.
Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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