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New Insights into Hyperspectral Image Processing Methods in Remote Sensing
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New Insights into Hyperspectral Image Processing Methods in Remote Sensing

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 (1 May 2022) | Viewed by 16699

Special Issue Editors

Dr. W. David Pan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
Interests: machine learning; data compression; signal and image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Turgay Celik

E-Mail Website
Guest Editor
1. School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2000, South Africa
2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
Interests: signal/image/video processing; visual computing; machine learning; cognitive computing; remote sensing data modelling and processing
Special Issues, Collections and Topics in MDPI journals
Dr. Joel Fu

E-Mail Website
Guest Editor
Department of Computer Science, Alabama A&M University, Normal, AL 35762, USA
Interests: cyber security; image processing; sensor/data fusion; target discrimination

Special Issue Information

Dear Colleagues,

Hyperspectral imaging technologies have been widely used in many remote sensing applications, such as agricultural activities, urban planning, and environment monitoring. While the COVID-19 pandemic has led to a slight decline of the industries of the respective applications, with the resuming of businesses, the global market for hyperspectral systems is expected to enjoy an exponential growth in the near future. Hyperspectral remote sensing systems are generating increasingly more voluminous high-dimensional datasets with finer resolutions, which require more efficient methods for image processing and analysis, pattern recognition and target detection, as well as parallel and hardware implementation to support real-time applications.

In this Special Issue, we solicit original contributions that provide new insights on the following topics:

  • hyperspectral image fusion and registration;
  • spatial/spectral/temporal analysis of hyperspectral images;
  • statistical and geometric modeling of hyperspectral images;
  • compressive sensing, and data compression methods for hyperspectral images;
  • scene classification and object detection;
  • machine learning and knowledge-based methods;
  • hardware and parallel implementation methods;
  • new applications of hyperspectral imaging for remote sensing.

Please note that the above list is not exclusive; papers on related topics will also be considered.

Dr. David Pan
Prof. Dr. Turgay Celik
Dr. Joel Fu
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 images
  • image model, processing, and analysis
  • image fusion and registration
  • pattern recognition and machine learning
  • data compression
  • implementations

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Published Papers (6 papers)

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Research

24 pages, 4713 KiB  
Article
Hyperspectral Remote Sensing Image Classification Based on Partitioned Random Projection Algorithm
by Shuhan Jia, Quanhua Zhao and Yu Li
Remote Sens. 2022, 14(9), 2194; https://doi.org/10.3390/rs14092194 - 4 May 2022
Viewed by 1937
Abstract
Dimensionality reduction based on random projection (RP) includes two problems, namely, the dimensionality is limited by the data size and the class separability of the dimensionality reduction results is unstable due to the randomly generated projection matrix. These problems make the RP algorithm [...] Read more.
Dimensionality reduction based on random projection (RP) includes two problems, namely, the dimensionality is limited by the data size and the class separability of the dimensionality reduction results is unstable due to the randomly generated projection matrix. These problems make the RP algorithm unsuitable for large-size hyperspectral image (HSI) classification. To solve these problems, this paper presents a new partitioned RP (PRP) algorithm and proves its rationality in theory. First, a large-size HSI is evenly divided into multiple small-size sub-HSIs. Afterwards, the projection matrix that maximizes the class separability is selected from multiple samplings in which the class dissimilarity measurement is defined as large inter-class distance and small intra-class variance. By using the same projection matrix, each small-size sub-HSI is projected to generate a low dimensional sub-HSI, thereby generating a low dimensional HSI. Next, the minimum distance (MD) classifier is utilized to classify the low dimensional HSI obtained by the PRP algorithm. Finally, four real HSIs are used for experiments, and three of the most popular classification algorithms based on RP are selected as comparison algorithms to validate the effectiveness of the proposed algorithm. The classification performance is evaluated with the kappa coefficient, overall accuracy (OA), average accuracy (AA), average precision rate (APR), and running time. Experimental results indicate that the proposed algorithm can obtain reliable classification results in a very short time. Full article
(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)
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25 pages, 8842 KiB  
Article
Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision
by Sheng Lin, Min Zhang, Xi Cheng, Liang Wang, Maiping Xu and Hai Wang
Remote Sens. 2022, 14(8), 1784; https://doi.org/10.3390/rs14081784 - 7 Apr 2022
Cited by 29 | Viewed by 2834
Abstract
Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the [...] Read more.
Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods. Full article
(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)
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22 pages, 4653 KiB  
Article
A Novel Method for Fast Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction
by Tianru Xue, Yueming Wang and Xuan Deng
Remote Sens. 2022, 14(7), 1737; https://doi.org/10.3390/rs14071737 - 4 Apr 2022
Cited by 3 | Viewed by 1917
Abstract
Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonlinear features within HSIs. It adopts the kernel function to ensure [...] Read more.
Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonlinear features within HSIs. It adopts the kernel function to ensure data linear separability by transforming the original data to a higher feature space, following which a linear analysis can be performed in this space. However, KMNF transformation has the problem of high computational complexity and low execution efficiency. It is not suitable for the processing of large-scale datasets. In terms of this problem, this paper proposes a graphics processing unit (GPU) and Nyström method-based algorithm for Fast KMNF transformation (GNKMNF). First, the Nyström method estimates the eigenvector of the entire kernel matrix in KMNF transformation by the decomposition and extrapolation of the sub-kernel matrix to reduce the computational complexity. Then, the sample size in the Nyström method is determined utilizing a proportional gradient selection strategy. Finally, GPU parallel computing is employed to further improve the execution efficiency. Experimental results show that compared with KMNF transformation, improvements of up to 1.94% and 2.04% are achieved by GNKMNF in overall classification accuracy and Kappa, respectively. Moreover, with a data size of 64 × 64 × 250, the execution efficiency of GNKMNF speeds up by about 80×. The outcome demonstrates the significant performance of GNKMNF in feature extraction and execution efficiency. Full article
(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)
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23 pages, 4301 KiB  
Article
Small Sample Hyperspectral Image Classification Based on Cascade Fusion of Mixed Spatial-Spectral Features and Second-Order Pooling
by Fan Feng, Yongsheng Zhang, Jin Zhang and Bing Liu
Remote Sens. 2022, 14(3), 505; https://doi.org/10.3390/rs14030505 - 21 Jan 2022
Cited by 17 | Viewed by 2908
Abstract
Hyperspectral images can capture subtle differences in reflectance of features in hundreds of narrow bands, and its pixel-wise classification is the cornerstone of many applications requiring fine-grained classification results. Although three-dimensional convolutional neural networks (3D-CNN) have been extensively investigated in hyperspectral image classification [...] Read more.
Hyperspectral images can capture subtle differences in reflectance of features in hundreds of narrow bands, and its pixel-wise classification is the cornerstone of many applications requiring fine-grained classification results. Although three-dimensional convolutional neural networks (3D-CNN) have been extensively investigated in hyperspectral image classification tasks and have made significant breakthroughs, hyperspectral classification under small sample conditions is still challenging. In order to facilitate small sample hyperspectral classification, a novel mixed spatial-spectral features cascade fusion network (MSSFN) is proposed. First, the covariance structure of hyperspectral data is modeled and dimensionality reduction is conducted using factor analysis. Then, two 3D spatial-spectral residual modules and one 2D separable spatial residual module are used to extract mixed spatial-spectral features. A cascade fusion pattern consisting of intra-block feature fusion and inter-block feature fusion is constructed to enhance the feature extraction capability. Finally, the second-order statistical information of the fused features is mined using second-order pooling and the classification is achieved by the fully connected layer after L2 normalization. On the three public available hyperspectral datasets, Indian Pines, Houston, and University of Pavia, only 5%, 3%, and 1% of the labeled samples were used for training, the accuracy of MSSFN in this paper is 98.52%, 96.31% and 98.83%, respectively, which is far better than the contrast models and verifies the effectiveness of MSSFN in small sample hyperspectral classification tasks. Full article
(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)
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17 pages, 2443 KiB  
Article
A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification
by Bishwas Praveen and Vineetha Menon
Remote Sens. 2022, 14(1), 217; https://doi.org/10.3390/rs14010217 - 4 Jan 2022
Cited by 9 | Viewed by 2805
Abstract
Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the [...] Read more.
Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on the rich spectral information present in the data, albeit all of the spectral information not being essential for hyperspectral data analysis. On the contrary, this redundant information present in the spectral bands can deter the performance of hyperspectral data analysis techniques. Therefore, in this work, we propose a novel bidirectional spectral attention mechanism, which is computationally efficient and capable of adaptive spectral information diversification through selective emphasis on spectral bands that comprise more information and suppress the ones with lesser information. The concept of 3D-convolutions in tandem with bidirectional long short-term memory (LSTM) is used in the proposed architecture as spectral attention mechanism. A feedforward neural network (FNN)-based supervised classification is then performed to validate the performance of our proposed approach. Experimental results reveal that the proposed hyperspectral data analysis model with spectral attention mechanism outperforms other spatial- and spectral-information-extraction-based hyperspectral data analysis techniques compared. Full article
(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)
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21 pages, 9386 KiB  
Article
Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification
by Tianyu Zhang, Cuiping Shi, Diling Liao and Liguo Wang
Remote Sens. 2021, 13(21), 4472; https://doi.org/10.3390/rs13214472 - 7 Nov 2021
Cited by 12 | Viewed by 2716
Abstract
Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral [...] Read more.
Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images. Full article
(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)
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