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Remote Sensing
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Imagery Classification and Feature Extraction Based on Hyperspectral Remote Sensing
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Imagery Classification and Feature Extraction Based on Hyperspectral 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: 30 June 2025 | Viewed by 5174

Special Issue Editors

Dr. Xia Xu

E-Mail Website
Guest Editor
School of Computer Science, Nankai University, Tianjin 300350, China
Interests: hyperspectral unmixing; remote sensing image processing; multi-objective optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Sen Lei

E-Mail Website
Guest Editor
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
Interests: remote sensing image super-resolution; deep learning; multi-modal learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanchao Su

E-Mail Website
Guest Editor
Department of Remote Sensing, College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: hyperspectral image classification; hyperspectral unmixing; hyperspectral image super-resolution; deep learning; neural networks
Dr. Xuanwen Tao

E-Mail Website
Guest Editor
Imec-Vision Lab, Department of Physics, University of Antwerp, B-2610 Antwerp, Belgium
Interests: hyperspectral unmixing; hyperspectral classification; hyperspectral anomaly detection; machine learning

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing can provide abundant spectral information on objects, thereby realizing sub-pixel and material-level identifications. As such, it plays a huge role in precision agriculture, national defense and military, water quality testing, mineral exploration, and other fields. In recent years, with the development of aerospace technology, the amount of earth observation data and data sources have increased day by day. The efficient processing and application breakthrough of remote sensing big data have become pain points and difficulties that urgently need to be solved. Especially under the current wave of large AI models, the collision of artificial intelligence and remote sensing will bring huge innovations to remote sensing related technologies and push the remote sensing industry into a new development cycle.

This Special Issue will focus on state-of-the-art or newly developed methods for the classification and feature extraction of hyperspectral remote sensing images. It will cover (but will not be limited to) the following topics:

  • Cutting-edge technologies for hyperspectral classification and feature extraction;
  • Large models for hyperspectral classification and feature extraction;
  • Application of hyperspectral classification and feature extraction in real scenarios;
  • Opportunities and challenges in hyperspectral remote sensing image processing.

Dr. Xia Xu
Dr. Sen Lei
Dr. Yuanchao Su
Dr. Xuanwen Tao
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
  • image classification
  • feature extraction
  • artificial intelligence
  • image processing

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Published Papers (4 papers)

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Research

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24 pages, 3462 KiB  
Article
Underutilized Feature Extraction Methods for Burn Severity Mapping: A Comprehensive Evaluation
by Linh Nguyen Van and Giha Lee
Remote Sens. 2024, 16(22), 4339; https://doi.org/10.3390/rs16224339 - 20 Nov 2024
Cited by 2 | Viewed by 1152
Abstract
Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing [...] Read more.
Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM. Full article
(This article belongs to the Special Issue Imagery Classification and Feature Extraction Based on Hyperspectral Remote Sensing)
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23 pages, 4854 KiB  
Article
Ensemble Network-Based Distillation for Hyperspectral Image Classification in the Presence of Label Noise
by Youqiang Zhang, Ruihui Ding, Hao Shi, Jiaxi Liu, Qiqiong Yu, Guo Cao and Xuesong Li
Remote Sens. 2024, 16(22), 4247; https://doi.org/10.3390/rs16224247 - 14 Nov 2024
Viewed by 1419
Abstract
Deep learning has made remarkable strides in hyperspectral image (HSI) classification, significantly improving classification performance. However, the challenge of obtaining accurately labeled training samples persists, primarily due to the subjectivity of human annotators and their limited domain knowledge. This often results in erroneous [...] Read more.
Deep learning has made remarkable strides in hyperspectral image (HSI) classification, significantly improving classification performance. However, the challenge of obtaining accurately labeled training samples persists, primarily due to the subjectivity of human annotators and their limited domain knowledge. This often results in erroneous labels, commonly referred to as label noise. Such noisy labels can critically impair the performance of deep learning models, making it essential to address this issue. While previous studies focused on label noise filtering and label correction, these approaches often require estimating noise rates and may inadvertently propagate noisy labels to clean labels, especially in scenarios with high noise levels. In this study, we introduce an ensemble network-based distillation (END) method specifically designed to address the challenges posed by label noise in HSI classification. The core idea is to leverage multiple base neural networks to generate an estimated label distribution from the training data. This estimated distribution is then used alongside the ground-truth labels to train the target network effectively. Moreover, we propose a parameter-adaptive loss function that balances the impact of both the estimated and ground-truth label distributions during the training process. Our approach not only simplifies architectural requirements but also integrates seamlessly into existing deep learning frameworks. Comparative experiments on four hyperspectral datasets demonstrate the effectiveness of our method, highlighting its competitive performance in the presence of label noise. Full article
(This article belongs to the Special Issue Imagery Classification and Feature Extraction Based on Hyperspectral Remote Sensing)
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18 pages, 4937 KiB  
Article
Large-Kernel Central Block Masked Convolution and Channel Attention-Based Reconstruction Network for Anomaly Detection of High-Resolution Hyperspectral Imagery
by Qiong Ran, Hong Zhong, Xu Sun, Degang Wang and He Sun
Remote Sens. 2024, 16(22), 4125; https://doi.org/10.3390/rs16224125 - 5 Nov 2024
Viewed by 958
Abstract
In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and [...] Read more.
In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small spatial neighborhoods for reconstruction. As a result, large anomalous targets and background details are often well reconstructed, leading to poor anomaly detection performance, as these targets are not sufficiently distinguished from the background. To address these limitations, we propose a novel HAD network for HRHSI based on large-kernel central block masked convolution and channel attention, termed LKCMCA. Specifically, we first employ the pixel-shuffle technique to reduce the size of anomalous targets without losing image information. Next, we design a large-kernel central block masked convolution to make the network pay more attention to the surrounding background information, enabling better fusion of the information between adjacent bands. This, coupled with an efficient channel attention mechanism, allows the network to capture deeper spectral features, enhancing the reconstruction of the background while suppressing anomalous targets. Furthermore, we introduce an adaptive loss function by down-weighting anomalous pixels based on the mean absolute error. This loss function is specifically designed to suppress the reconstruction of potentially anomalous pixels during network training, allowing our model to be considered an excellent background reconstruction network. By leveraging reconstruction error, the model effectively highlights anomalous targets. Meanwhile, we produced four benchmark datasets specifically for HAD tasks using existing HRHSI data, addressing the current shortage of HRHSI datasets in the HAD field. Extensive experiments demonstrate that our LKCMCA method achieves superior detection performance, outperforming ten state-of-the-art HAD methods on all datasets. Full article
(This article belongs to the Special Issue Imagery Classification and Feature Extraction Based on Hyperspectral Remote Sensing)
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18 pages, 9538 KiB  
Technical Note
Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection
by Huan Zhang, Xiaolin Han and Weidong Sun
Remote Sens. 2024, 16(24), 4806; https://doi.org/10.3390/rs16244806 - 23 Dec 2024
Viewed by 759
Abstract
As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. Although the current data augmentation methods are efficient because they force the network to learn multiple parts [...] Read more.
As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. Although the current data augmentation methods are efficient because they force the network to learn multiple parts of a given training image through occlusion or re-editing, most of them can damage the internal structures of targets and ultimately affect the results of subsequent application tasks. To this end, region-focusing data augmentation via salient region activation and bitplane recombination for the target detection of optical satellite images is proposed in this paper to solve the problem of internal structure loss in data augmentation. More specifically, to boost the utilization of the positive regions and typical negative regions, a new surroundedness-based strategy for salient region activation is proposed, through which new samples with meaningful focusing regions can be generated. And to generate new samples of the focusing regions, a region-based strategy for bitplane recombination is also proposed, through which internal structures of the focusing regions can be reserved. Thus, a multiplied effect of data augmentation by the two strategies can be achieved. In addition, this is the first time that data augmentation has been examined from the perspective of meaningful focusing regions, rather than the whole sample image. Experiments on target detection with public datasets have demonstrated the effectiveness of this proposed method, especially for small targets. Full article
(This article belongs to the Special Issue Imagery Classification and Feature Extraction Based on Hyperspectral Remote Sensing)
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