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Remote Sensing
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Advances of Hyperspectral Imaging Data Applications in Land Monitoring
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Advances of Hyperspectral Imaging Data Applications in Land Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 13710

Special Issue Editors

Prof. Dr. Wei Li

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: multi-source remote sensing data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Na Liu

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: digital signal processing; signal processing; signal, image and video processing; image processing; digital image processing; wavelet analysis; image enhancement; image fusion; image analysis; machine learning
Prof. Dr. Jocelyn Chanussot

E-Mail Website
Guest Editor
Gipsa-Lab, Grenoble Institute of Technology, 38031 Grenoble, France
Interests: image analysis; hyperspectral remote sensing; data fusion; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qian Du

grade E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years we have witnessed large technological requirements for land monitoring, which has provided humankind with a plethora of information about land cover/use, changes, and bio-geophysical parameters at different scales. In particular, the increase in the spectral resolution of hyperspectral imaging (HSI) has opened doors to advanced remotely sensed earth observation means. Hyperspectral imaging allows us to characterize the land objects of interest (e.g., land-cover classes) with unprecedented accuracy and to keep land usage inventories up to date. Meanwhile, improvements in spectral resolution and data volume have posed new methodological challenges, calling for advances in data processing and exploitation algorithms.

The main focuses in this area—which have recently gained popularity, attracting the interest of multiple scientific disciplines—include hyperspectral imaging restoration/super-resolution, multi-source registration and fusion, information/feature extraction, pixel-level classification, object-level segmentation/recognition, change detection, high-resolution land mapping, etc.

Sophisticated hyperspectral imaging platforms and sensors are being launched to capture HSI data with higher spatial, spectral, and temporal resolutions. Corresponding data-processing packages and application products are also being released to facilitate the powerful abilities to conduct land-monitoring applications.

This Special Issue aims to review and synthesize the latest progress in land monitoring using hyperspectral imaging data for various application purposes. Prospective authors are invited to submit original manuscripts to this Special Issue of Remote Sensing.

Prof. Dr. Wei Li
Dr. Na Liu
Prof. Dr. Jocelyn Chanussot
Prof. Dr. Qian Du
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

  • feature extraction
  • harsh land monitoring environments
  • high-resolution land mapping
  • hyperspectral imaging restoration
  • information fusion
  • land classification
  • multi-source registration
  • object recognition
  • super-resolution
  • spectral imaging

Published Papers (7 papers)

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Research

21 pages, 4951 KiB  
Article
Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels
by Youqiang Zhang, Jin Sun, Hao Shi, Zixian Ge, Qiqiong Yu, Guo Cao and Xuesong Li
Remote Sens. 2023, 15(10), 2543; https://doi.org/10.3390/rs15102543 - 12 May 2023
Cited by 1 | Viewed by 1122
Abstract
Deep learning-based label noise learning methods provide promising solutions for hyperspectral image (HSI) classification with noisy labels. Currently, label noise learning methods based on deep learning improve their performance by modifying one aspect, such as designing a robust loss function, revamping the network [...] Read more.
Deep learning-based label noise learning methods provide promising solutions for hyperspectral image (HSI) classification with noisy labels. Currently, label noise learning methods based on deep learning improve their performance by modifying one aspect, such as designing a robust loss function, revamping the network structure, or adding a noise adaptation layer. However, these methods face difficulties in coping with relatively high noise situations. To address this issue, this paper proposes a unified label noise learning framework with a dual-network structure. The goal is to enhance the model’s robustness to label noise by utilizing two networks to guide each other. Specifically, to avoid the degeneration of the dual-network training into self-training, the “disagreement” strategy is incorporated with co-learning. Then, the “agreement” strategy is introduced into the model to ensure that the model iterates in the right direction under high noise conditions. To this end, an agreement and disagreement-based co-learning (ADCL) framework is proposed for HSI classification with noisy labels. In addition, a joint loss function consisting of a supervision loss of two networks and a relative loss between two networks is designed for the dual-network structure. Extensive experiments are conducted on three public HSI datasets to demonstrate the robustness of the proposed method to label noise. Specifically, our method obtains the highest overall accuracy of 98.62%, 90.89%, and 99.02% on the three datasets, respectively, which represents an improvement of 2.58%, 2.27%, and 0.86% compared to the second-best method. In future research, the authors suggest using more networks as backbones to implement the ADCL framework. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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22 pages, 5906 KiB  
Article
Camouflaged Object Detection Based on Ternary Cascade Perception
by Xinhao Jiang, Wei Cai, Yao Ding, Xin Wang, Zhiyong Yang, Xingyu Di and Weijie Gao
Remote Sens. 2023, 15(5), 1188; https://doi.org/10.3390/rs15051188 - 21 Feb 2023
Cited by 5 | Viewed by 2353
Abstract
Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the [...] Read more.
Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)50 that reaches 91.41, an AP75 that improves to 73.04, and significantly higher detection accuracy and confidence. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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20 pages, 2328 KiB  
Article
Bi-Kernel Graph Neural Network with Adaptive Propagation Mechanism for Hyperspectral Image Classification
by Haojie Hu, Yao Ding, Fang He, Fenggan Zhang, Jianwei Zhao and Minli Yao
Remote Sens. 2022, 14(24), 6224; https://doi.org/10.3390/rs14246224 - 08 Dec 2022
Cited by 3 | Viewed by 1398
Abstract
Graph neural networks (GNNs) have been widely applied for hyperspectral image (HSI) classification, due to their impressive representation ability. It is well-known that typical GNNs and their variants work under the assumption of homophily, while most existing GNN-based HSI classification methods neglect the [...] Read more.
Graph neural networks (GNNs) have been widely applied for hyperspectral image (HSI) classification, due to their impressive representation ability. It is well-known that typical GNNs and their variants work under the assumption of homophily, while most existing GNN-based HSI classification methods neglect the heterophily that is widely present in the constructed graph structure. To deal with this problem, a homophily-guided Bi-Kernel Graph Neural Network (BKGNN) is developed for HSI classification. In the proposed BKGNN, we estimate the homophily between node pairs according to a learnable homophily degree matrix, which is then applied to change the propagation mechanism by adaptively selecting two different kernels to capture homophily and heterophily information. Meanwhile, the learning process of the homophily degree matrix and the bi-kernel feature propagation process are trained jointly to enhance each other in an end-to-end fashion. Extensive experiments on three public data sets demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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22 pages, 3133 KiB  
Article
Weighted Local Ratio-Difference Contrast Method for Detecting an Infrared Small Target against Ground–Sky Background
by Hongguang Wei, Pengge Ma, Dongdong Pang, Wei Li, Jinwang Qian and Xingchen Guo
Remote Sens. 2022, 14(22), 5636; https://doi.org/10.3390/rs14225636 - 08 Nov 2022
Cited by 2 | Viewed by 1418
Abstract
Fast and robust detection of infrared small targets in a single image has always been challenging. The background residue in complex ground–sky background images leads to high false alarm rates when traditional local contrast methods are used because of the complexity and variability [...] Read more.
Fast and robust detection of infrared small targets in a single image has always been challenging. The background residue in complex ground–sky background images leads to high false alarm rates when traditional local contrast methods are used because of the complexity and variability of the ground–sky background imaging environment. A weighted local ratio-difference contrast (WLRDC) method is proposed in this paper to address this problem and detect infrared small targets in the ground–sky background. First, target candidate pixels are obtained using a simple facet kernel filter. Second, local contrast saliency maps and weighted mappings are calculated on the basis of the local ratio-difference contrast and the spatial dissimilarity of the target, respectively. Third, the final weighted mapping can be obtained through the multiplication fusion strategy. Finally, a simple threshold segmentation method is employed to extract the target. Experimental results on six real ground–sky infrared scenes showed that the proposed method outperforms existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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21 pages, 13540 KiB  
Article
Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique
by Yan Huang, Xiao Zhou, Bobo Xi, Jiaojiao Li, Jian Kang, Shiyang Tang, Zhanye Chen and Wei Hong
Remote Sens. 2022, 14(12), 2907; https://doi.org/10.3390/rs14122907 - 17 Jun 2022
Cited by 4 | Viewed by 1624
Abstract
In this paper, a diverse-region hyperspectral image classification (DRHy) method is proposed by considering both irregularly local pixels and globally contextual connections between pixels. Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions [...] Read more.
In this paper, a diverse-region hyperspectral image classification (DRHy) method is proposed by considering both irregularly local pixels and globally contextual connections between pixels. Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions to cluster irregularly local-region pixels. In addition, the dimensionality reduction method is employed to alleviate the curse of dimensionality problem with a lower computational burden, generating more representative data with the input graph features. In this context, it then constructs a superpixelwise Chebyshev polynomial graph convolution network (ChebyNet) to aggregate global-region superpixels. Benefiting from different superpixel numbers of segmentations, we construct different graph structures, and multiple classification results are obtained, which brings more opportunities to represent the hyperspectral data correctly. Then, all the diverse-region results are further fused by a majority voting technique to improve the final performance. Finally, numerical experiments on two benchmark datasets are provided to demonstrate the superiority of the proposed DRHy-ChebyNet method to the other state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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24 pages, 9334 KiB  
Article
Monitoring the Invasive Plant Spartina alterniflora in Jiangsu Coastal Wetland Using MRCNN and Long-Time Series Landsat Data
by Wenqing Zhu, Guangbo Ren, Jianping Wang, Jianbu Wang, Yabin Hu, Zhaoyang Lin, Wei Li, Yajie Zhao, Shibao Li and Ning Wang
Remote Sens. 2022, 14(11), 2630; https://doi.org/10.3390/rs14112630 - 31 May 2022
Cited by 13 | Viewed by 2253
Abstract
Jiangsu coastal wetland has the largest area of the invasive plant, Spartina alterniflora (S. alterniflora), in China. S. alterniflora has been present in the wetland for nearly 40 years and poses a substantial threat to the safety of coastal wetland ecosystems. [...] Read more.
Jiangsu coastal wetland has the largest area of the invasive plant, Spartina alterniflora (S. alterniflora), in China. S. alterniflora has been present in the wetland for nearly 40 years and poses a substantial threat to the safety of coastal wetland ecosystems. There is an urgent need to control the distribution of S. alterniflora. The biological characteristics of the invasion process of S. alterniflora contribute to its multi-scale distribution. However, the current classification methods do not deal successfully with multi-scale problems, and it is also difficult to perform high-precision land cover classification on multi-temporal remote sensing images. In this study, based on Landsat data from 1990 to 2020, a new deep learning multi-scale residual convolutional neural network (MRCNN) model was developed to identify S. alterniflora. In this method, features at different scales are extracted and concatenated to obtain multi-scale information, and residual connections are introduced to ensure gradient propagation. A multi-year data unified training method was adopted to improve the temporal scalability of the MRCNN. The MRCNN model was able to identify the annual S. alterniflora distribution more accurately, overcame the disadvantage that traditional CNNs can only extract feature information at a single scale, and offered significant advantages in spatial characterization. A thematic map of S. alterniflora distribution was obtained. Since it was introduced in 1982, the distribution of S. alterniflora has expanded to approximately 17,400 ha. In Jiangsu, the expansion process of S. alterniflora over time was divided into three stages: the growth period (1982–1994), the outbreak period (1995–2004), and the plateau period (2005–2020). The spatial expansion direction was mainly parallel and perpendicular to the coastline. The hydrodynamic conditions and tidal flat environment on the coast of Jiangsu Province are suitable for the growth of S. alterniflora. Reclamation of tidal flats is the main factor affecting the expansion of S. alterniflora. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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23 pages, 4804 KiB  
Article
Integrating Hybrid Pyramid Feature Fusion and Coordinate Attention for Effective Small Sample Hyperspectral Image Classification
by Chen Ding, Youfa Chen, Runze Li, Dushi Wen, Xiaoyan Xie, Lei Zhang, Wei Wei and Yanning Zhang
Remote Sens. 2022, 14(10), 2355; https://doi.org/10.3390/rs14102355 - 13 May 2022
Cited by 10 | Viewed by 2100
Abstract
In recent years, hyperspectral image (HSI) classification (HSIC) methods that use deep learning have proved to be effective. In particular, the utilization of convolutional neural networks (CNNs) has proved to be highly effective. However, some key issues need to be addressed when classifying [...] Read more.
In recent years, hyperspectral image (HSI) classification (HSIC) methods that use deep learning have proved to be effective. In particular, the utilization of convolutional neural networks (CNNs) has proved to be highly effective. However, some key issues need to be addressed when classifying hyperspectral images (HSIs), such as small samples, which can influence the generalization ability of the CNNs and the HSIC results. To address this problem, we present a new network that integrates hybrid pyramid feature fusion and coordinate attention for enhancing small sample HSI classification results. The innovative nature of this paper lies in three main areas. Firstly, a baseline network is designed. This is a simple hybrid 3D-2D CNN. Using this baseline network, more robust spectral-spatial feature information can be obtained from the HSI. Secondly, a hybrid pyramid feature fusion mechanism is used, meaning that the feature maps of different levels and scales can be effectively fused to enhance the feature extracted by the model. Finally, coordinate attention mechanisms are utilized in the network, which can not only adaptively capture the information of the spectral dimension, but also include the direction-aware and position sensitive information. By doing this, the proposed CNN structure can extract more useful HSI features and effectively be generalized to test samples. The proposed method was shown to obtain better results than several existing methods by experimenting on three public HSI datasets. Full article
(This article belongs to the Special Issue Advances of Hyperspectral Imaging Data Applications in Land Monitoring)
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