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Remote Sensing
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Deep Learning for Hyperspectral Image Classification
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Deep Learning for Hyperspectral Image Classification

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 June 2023) | Viewed by 8526

Special Issue Editors

Dr. Yu Oishi

E-Mail Website
Guest Editor
Core Technology Research Headquaters, National Agriculture and Food Research Organization, Tsukuba 305-0856, Japan
Interests: hyperspectral RS; plant disease diagnosis; animal remote sensing; cloud mask
Special Issues, Collections and Topics in MDPI journals
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

Special Issue Information

Dear Colleagues,

The number of satellite hyperspectral sensors to monitor greenhouse gases, ocean colors, minerals, animals, and so on is increasing. Similarly, although expectations for drone hyperspectral imaging are increasing, in the past, drone hyperspectral sensors could not obtain good-quality aligned images due to the movement of the drone during scanning.

Now, however, there are sensors that can solve the above problem, such as sensors with spectral filters attached to each pixel of the light-receiving element, which enable hyperspectral drone observations.

Meanwhile, applications of deep learning are popular in generic image recognition. Although it used to be necessary to determine the features and their thresholds used for image recognition, for example, supervised deep learning models learn and determine them from training images. In the result, it is inferred that features that have been difficult to formulate are also used.

Hyperspectral images contain vast amounts of information. There are also methods of determining and analyzing the absorption bands to be used in advance, but the use of deep learning is expected to increase the number of applications using hyperspectral images.

Although the focus of this Special Issue is deep learning for hyperspectral image, accompanying technologies and applications are also acceptable, e.g., methods for obtaining good-quality aligned images with line-scanning hyperspectral sensors on the drone. This Special Issue welcomes techniques and experimental research articles on the following topics, although progress reports on relevant research issues are also acceptable.

Dr. Yu Oishi
Dr. David Pan
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

  • hyperspectral remote sensing
  • deep learning
  • hyperspectral image
  • hyperspectral satellite sensor
  • hyperspectral drone sensor
  • hyperspectrometer
  • spectral analysis
  • image recognition
  • data fusion
  • new sensors
  • applications

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Published Papers (3 papers)

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Research

21 pages, 21557 KiB  
Article
A Feature Embedding Network with Multiscale Attention for Hyperspectral Image Classification
by Yi Liu, Jian Zhu, Jiajie Feng and Caihong Mu
Remote Sens. 2023, 15(13), 3338; https://doi.org/10.3390/rs15133338 - 29 Jun 2023
Cited by 4 | Viewed by 1964
Abstract
In recent years, convolutional neural networks (CNNs) have been widely used in the field of hyperspectral image (HSI) classification and achieved good classification results due to their excellent spectral–spatial feature extraction ability. However, most methods use the deep semantic features at the end [...] Read more.
In recent years, convolutional neural networks (CNNs) have been widely used in the field of hyperspectral image (HSI) classification and achieved good classification results due to their excellent spectral–spatial feature extraction ability. However, most methods use the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To solve the above problems, this article proposes a hyperspectral image classification method based on a Feature Embedding Network with Multiscale Attention (MAFEN). Firstly, a Multiscale Attention Module (MAM) is designed, which is able to not only learn multiscale information about features at different depths, but also extract effective information from them. Secondly, the deep semantic features can be embedded into the low-level features through the top-down channel, so that the features at all levels have rich semantic information. Finally, an Adaptive Spatial Feature Fusion (ASFF) strategy is introduced to adaptively fuse features from different levels. The experimental results show that the classification accuracies of MAFEN on four HSI datasets are better than those of the compared methods. Full article
(This article belongs to the Special Issue Deep Learning for Hyperspectral Image Classification)
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25 pages, 31641 KiB  
Article
A Cross-Channel Dense Connection and Multi-Scale Dual Aggregated Attention Network for Hyperspectral Image Classification
by Haiyang Wu, Cuiping Shi, Liguo Wang and Zhan Jin
Remote Sens. 2023, 15(9), 2367; https://doi.org/10.3390/rs15092367 - 29 Apr 2023
Cited by 17 | Viewed by 2677
Abstract
Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral images in the case of small samples. To [...] Read more.
Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral images in the case of small samples. To alleviate this problem, in this paper, a dual-branch network which combines cross-channel dense connection and multi-scale dual aggregated attention (CDC_MDAA) is proposed. On the spatial branch, a cross-channel dense connections (CDC) module is designed. The CDC can effectively combine cross-channel convolution with dense connections to extract the deep spatial features of HSIs. Then, a spatial multi-scale dual aggregated attention module (SPA_MDAA) is constructed. The SPA_MDAA adopts dual autocorrelation for attention modeling to strengthen the differences between features and enhance the ability to pay attention to important features. On the spectral branch, a spectral multi-scale dual aggregated attention module (SPE_MDAA) is designed to capture important spectral features. Finally, the spatial spectral features are fused, and the classification results are obtained. The experimental results show that the classification performance of the proposed method is superior to some state-of-the-art methods in small samples and has good generalization. Full article
(This article belongs to the Special Issue Deep Learning for Hyperspectral Image Classification)
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25 pages, 5456 KiB  
Article
Local and Global Spectral Features for Hyperspectral Image Classification
by Zeyu Xu, Cheng Su, Shirou Wang and Xiaocan Zhang
Remote Sens. 2023, 15(7), 1803; https://doi.org/10.3390/rs15071803 - 28 Mar 2023
Cited by 7 | Viewed by 3059
Abstract
Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression [...] Read more.
Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression and utilization of the spectrum. Traditional HSI feature extraction methods design spectral features manually, which is likely to be limited by the complex spectral information within HSI. Recently, data-driven methods, especially the use of convolutional neural networks (CNNs), have shown great improvements in performance when processing image data owing to their powerful automatic feature learning and extraction abilities and are also widely used for HSI feature extraction and classification. The CNN extracts features based on the convolution operation. Nevertheless, the local perception of the convolution operation makes CNN focus on the local spectral features (LSF) and weakens the description of features between long-distance spectral ranges, which will be referred to as global spectral features (GSF) in this study. LSF and GSF describe the spectral features from two different perspectives and are both essential for determining the spectrum. Thus, in this study, a local-global spectral feature (LGSF) extraction and optimization method is proposed to jointly consider the LSF and GSF for HSI classification. To increase the relationship between spectra and the possibility to obtain features with more forms, we first transformed the 1D spectral vector into a 2D spectral image. Based on the spectral image, the local spectral feature extraction module (LSFEM) and the global spectral feature extraction module (GSFEM) are proposed to automatically extract the LGSF. The loss function for spectral feature optimization is proposed to optimize the LGSF and obtain improved class separability inspired by contrastive learning. We further enhanced the LGSF by introducing spatial relation and designed a CNN constructed using dilated convolution for classification. The proposed method was evaluated on four widely used HSI datasets, and the results highlighted its comprehensive utilization of spectral information as well as its effectiveness in HSI classification. Full article
(This article belongs to the Special Issue Deep Learning for Hyperspectral Image Classification)
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