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Remote Sensing
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Deep Learning for the Analysis of Multi-/Hyperspectral Images II
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Deep Learning for the Analysis of Multi-/Hyperspectral Images II

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 October 2024 | Viewed by 3112

Special Issue Editors

Prof. Dr. Junjun Jiang

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Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: image super-resolution; image denoising; video processing; hyperspectral image analysis; image fusion; visual recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Bihan Wen

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Guest Editor
School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU), Singapore 639798, Singapore
Interests: machine learning; image processing; computational imaging; computer vision; inverse problems
Special Issues, Collections and Topics in MDPI journals
Dr. Kui Jiang

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Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: computer vision and video; image processing
Prof. Dr. Leyuan Fang

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Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: deep learning; remote sensing image processing and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiayi Ma

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Guest Editor
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: computer vision; machine learning; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Gemine Vivone

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Guest Editor
Institute of Methodologies for Environmental Analysis, CNR-IMAA, 85050 Tito Scalo, Italy
Interests: statistical signal processing; detection of remotely sensed images; data fusion; tracking algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the Special Issue “Deep Learning for the Analysis of Multi-/Hyperspectral Images”, a new Special Issue is being organized.

Unlike human eyes, which can only be exposed to visible light, multi-/hyperspectral imaging is an imaging technique used for the collection and processing of information across a large portion of the electromagnetic spectrum. Multi-/hyperspectral images have strong spectral diagnostic potential to distinguish materials that, to humans, look similar. Over the past few years, deep learning has been powering many aspects of remote sensing image processing applications ranging from low-level restoration to high-level analysis, and remarkable breakthroughs have been achieved using deep-learning-based approaches.

This Special Issue will publish manuscripts that present new deep learning models or introduce the most advanced deep networks for processing and analyzing multi-/hyperspectral images. As this is a broad area, there are no constraints regarding the field of application. Articles for this Special Issue on deep learning for the analysis of multi-/hyperspectral images may address, but are not limited to, the following topics:

  • Spatial/spectral super-resolution;
  • Image fusion/pansharpening;
  • Image denoising/destriping;
  • Image registration/matching;
  • Compressive sensing;
  • Computational imaging;
  • Image/sense classification;
  • Object detection;
  • Clustering;
  • Segmentation

Prof. Dr. Junjun Jiang
Dr. Bihan Wen
Dr. Kui Jiang
Prof. Dr. Leyuan Fang
Prof. Dr. Jiayi Ma
Dr. Gemine Vivone
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

  • deep learning
  • neural network
  • image processing
  • image analysis
  • multispectral image
  • hyperspectral image

Published Papers (3 papers)

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Research

21 pages, 2266 KiB  
Article
MEA-EFFormer: Multiscale Efficient Attention with Enhanced Feature Transformer for Hyperspectral Image Classification
by Qian Sun, Guangrui Zhao, Yu Fang, Chenrong Fang, Le Sun and Xingying Li
Remote Sens. 2024, 16(9), 1560; https://doi.org/10.3390/rs16091560 - 27 Apr 2024
Viewed by 312
Abstract
Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance [...] Read more.
Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance. Full article
(This article belongs to the Special Issue Deep Learning for the Analysis of Multi-/Hyperspectral Images II)
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21 pages, 10212 KiB  
Article
Dynamic Task Planning Method for Multi-Source Remote Sensing Satellite Cooperative Observation in Complex Scenarios
by Qianyu Wu, Jun Pan and Mi Wang
Remote Sens. 2024, 16(4), 657; https://doi.org/10.3390/rs16040657 - 10 Feb 2024
Cited by 1 | Viewed by 904
Abstract
As the number and variety of remote sensing satellites continue to grow, user demands are becoming increasingly complex and diverse. Concurrently, there is an escalating requirement for timeliness in satellite observations, thereby augmenting the complexity of task processing and resource allocation. In response [...] Read more.
As the number and variety of remote sensing satellites continue to grow, user demands are becoming increasingly complex and diverse. Concurrently, there is an escalating requirement for timeliness in satellite observations, thereby augmenting the complexity of task processing and resource allocation. In response to these challenges, this paper proposes an innovative method for dynamic task planning in multi-source remote sensing satellite cooperative observations tailored to complex scenarios. In the task processing phase, this study develops a preprocessing model suitable for various types of targets, enabling the decomposition of complex scenes into multiple point targets for independent satellite observation, thereby reducing the complexity of the problem. In the resource allocation phase, a dynamic task planning algorithm for multi-satellite cooperative observation is designed to achieve dynamic and optimized scheduling of the processed point targets, catering to the needs of multi-source remote sensing satellites. Empirical validation demonstrated that this method effectively implements dynamic adjustment plans for point targets, comprehensively optimizing the number of observation targets, computation time, task priority, and satellite resource utilization, significantly enhancing the dynamic observation efficiency of remote sensing satellites. Full article
(This article belongs to the Special Issue Deep Learning for the Analysis of Multi-/Hyperspectral Images II)
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21 pages, 4454 KiB  
Article
Lightweight-VGG: A Fast Deep Learning Architecture Based on Dimensionality Reduction and Nonlinear Enhancement for Hyperspectral Image Classification
by Xuan Fei, Sijia Wu, Jianyu Miao, Guicai Wang and Le Sun
Remote Sens. 2024, 16(2), 259; https://doi.org/10.3390/rs16020259 - 09 Jan 2024
Viewed by 934
Abstract
In the past decade, deep learning methods have proven to be highly effective in the classification of hyperspectral images (HSI), consistently outperforming traditional approaches. However, the large number of spectral bands in HSI data can lead to interference during the learning process. To [...] Read more.
In the past decade, deep learning methods have proven to be highly effective in the classification of hyperspectral images (HSI), consistently outperforming traditional approaches. However, the large number of spectral bands in HSI data can lead to interference during the learning process. To address this issue, dimensionality reduction techniques can be employed to minimize data redundancy and improve HSI classification performance. Hence, we have developed an efficient lightweight learning framework consisting of two main components. Firstly, we utilized band selection and principal component analysis to reduce the dimensionality of HSI data, thereby reducing redundancy while retaining essential features. Subsequently, the pre-processed data was input into a modified VGG-based learning network for HSI classification. This method incorporates an improved dynamic activation function for the multi-layer perceptron to enhance non-linearity, and reduces the number of nodes in the fully connected layers of the original VGG architecture to improve speed while maintaining accuracy. This modified network structure, referred to as lightweight-VGG (LVGG), was specifically designed for HSI classification. Comprehensive experiments conducted on three publicly available HSI datasets consistently demonstrated that the LVGG method exhibited similar or better performance compared to other typical methods in the field of HSI classification. Our approach not only addresses the challenge of interference in deep learning methods for HSI classification, but also offers a lightweight and efficient solution for achieving high classification accuracy. Full article
(This article belongs to the Special Issue Deep Learning for the Analysis of Multi-/Hyperspectral Images II)
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