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Pattern Recognition in 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: closed (30 June 2023) | Viewed by 15849

Special Issue Editors

The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: remote sensing image quality improvement; hyperspectral imaging; computer vision

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Guest Editor
National Research Council - Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo, Italy
Interests: data fusion; target tracking; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last 30 years have witnessed the development of hyperspectral remote sensing devices and technologies for earth observation and pattern recognition. Supported by the hyperspectral sensors, the wealthy spectral, spatial and temporal information provide plenty of opportunities for fine-grained land cover classification, mineral mapping, water quality assessment, precious farming, urban planning and monitoring, disaster management and prediction, and concealed target detection. However, with the ever-increasing application requirements, new methodological challenges appear to meet the development of more advanced techniques for efficient feature extraction, model learning, and pattern recognition. For example, how to efficiently extract reliable features from low-quality hyperspectral remote sensing data from space, how to make the most use of large-scale data when the research area changes from region to country and world, and how to recognize and detect patterns from different types of sensors.

This Special Issue aims to explore state of the art in pattern recognition applications on hyperspectral remote sensing. 

Research contributions, as well as surveys, are welcome. Topics may cover advanced techniques to preprocessing, feature extraction, data fusion, cross-modality learning, material recognition, change detection, and so on. Articles may utilize advanced pattern recognition techniques to address, but are not limited, to the following topics:

  • Hyperspectral imaging
  • Hyperspectral image quality improvement
  • Hyperspectral feature extraction and selection
  • Data fusion and enhancement
  • Spectral unmixing
  • Multi-/cross-modal learning
  • Hyperspectral classification/segmentation/detection/recognition.

Dr. Wei He
Dr. Danfeng Hong
Dr. Naoto Yokoya
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

  • pattern recognition
  • hyperspectral remote sensing
  • feature extraction
  • hyperspectral imaging
  • hyperspectral restoration
  • cross-modal learning
  • data fusion
  • image enhancement

Published Papers (5 papers)

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Research

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16 pages, 7225 KiB  
Article
HCFPN: Hierarchical Contextual Feature-Preserved Network for Remote Sensing Scene Classification
by Jingwen Yuan and Shugen Wang
Remote Sens. 2023, 15(3), 810; https://doi.org/10.3390/rs15030810 - 31 Jan 2023
Cited by 2 | Viewed by 1182
Abstract
Convolutional neural networks (CNNs) have made significant advances in remote sensing scene classification (RSSC) in recent years. Nevertheless, the limitations of the receptive field cause CNNs to suffer from a disadvantage in capturing contextual information. To address this issue, vision transformer (ViT), a [...] Read more.
Convolutional neural networks (CNNs) have made significant advances in remote sensing scene classification (RSSC) in recent years. Nevertheless, the limitations of the receptive field cause CNNs to suffer from a disadvantage in capturing contextual information. To address this issue, vision transformer (ViT), a novel model that has piqued the interest of academics, is used to extract latent contextual information in remote sensing scene classification. However, when confronted with the challenges of large-scale variations and high interclass similarity in scene classification images, the original ViT has the drawback of ignoring important local features, thereby causing the model’s performance to degrade. Consequently, we propose the hierarchical contextual feature-preserved network (HCFPN) by combining the advantages of CNNs and ViT. First, a hierarchical feature extraction module based on ResNet-34 is utilized to acquire the multilevel convolutional features and high-level semantic features. Second, a contextual feature-preserved module takes advantage of the first two multilevel features to capture abundant long-term contextual features. Then, the captured long-term contextual features are utilized for multiheaded cross-level attention computing to aggregate and explore the correlation of multilevel features. Finally, the multiheaded cross-level attention score and high-level semantic features are classified. Then, a category score average module is proposed to fuse the classification results, whereas a label smoothing approach is utilized prior to calculating the loss to produce discriminative scene representation. In addition, we conduct extensive experiments on two publicly available RSSC datasets. Our proposed HCPFN outperforms most state-of-the-art approaches. Full article
(This article belongs to the Special Issue Pattern Recognition in Hyperspectral Remote Sensing)
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28 pages, 3692 KiB  
Article
Gaussian Process Modeling of In-Season Physiological Parameters of Spring Wheat Based on Airborne Imagery from Two Hyperspectral Cameras and Apparent Soil Electrical Conductivity
by Wiktor R. Żelazny, Krzysztof Kusnierek and Jakob Geipel
Remote Sens. 2022, 14(23), 5977; https://doi.org/10.3390/rs14235977 - 25 Nov 2022
Cited by 1 | Viewed by 1738
Abstract
The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop [...] Read more.
The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.750.85, RPDP=2.02.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2>0.8, RPDP>2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion. Full article
(This article belongs to the Special Issue Pattern Recognition in Hyperspectral Remote Sensing)
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24 pages, 6350 KiB  
Article
Spatial Sampling and Grouping Information Entropy Strategy Based on Kernel Fuzzy C-Means Clustering Method for Hyperspectral Band Selection
by Zhou Zhang, Degang Wang, Xu Sun, Lina Zhuang, Rong Liu and Li Ni
Remote Sens. 2022, 14(19), 5058; https://doi.org/10.3390/rs14195058 - 10 Oct 2022
Cited by 10 | Viewed by 1558
Abstract
The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. Clustering-based [...] Read more.
The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. Clustering-based methods can reduce band correlation significantly, but traditional clustering methods are mostly hard clustering and are not accurate enough to partition the bands. An unsupervised band selection method based on fuzzy c-means clustering (FCM) was introduced to tackle this problem. However, FCM can easily obtain the local optimal solution and take a long time to process high-dimensional data. Hence, this work applies kernel function and a sampling strategy to reduce calculation time, and information entropy is used to initialize the cluster center. A kernel FCM algorithm based on spatial sampling and a grouping information entropy strategy is proposed and called SSGIE-KFCM. This method not only optimizes the calculation process and reduces the amount of computation data, accelerating the calculation efficiency, but also adopts grouping information entropy to improve the probability of obtaining a global optimal solution. Classification experiments on two public HSI datasets show that: (1) The classification performance of the whole band can be achieved or even exceeded by using only a small number of bands to achieve the purpose of dimensionality reduction. (2) The classification accuracy can be improved compared with the FCM method. (3) With the introduction of sampling strategy and kernel function, the computational speed is at least 24 times faster than that of FCM. It has been proven that the SSGIE-KFCM method can significantly reduce the amount of HSI while retaining the primary information of the original data, which further promotes the research and application of HSI in the remote sensing area. Full article
(This article belongs to the Special Issue Pattern Recognition in Hyperspectral Remote Sensing)
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22 pages, 15285 KiB  
Article
Hyperspectral Image Mixed Noise Removal Using a Subspace Projection Attention and Residual Channel Attention Network
by Hezhi Sun, Ke Zheng, Ming Liu, Chao Li, Dong Yang and Jindong Li
Remote Sens. 2022, 14(9), 2071; https://doi.org/10.3390/rs14092071 - 26 Apr 2022
Cited by 3 | Viewed by 2209
Abstract
Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address [...] Read more.
Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed. Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise. By leveraging the local and global spatial relations, OSPA is able to reconstruct the local structure of the feature maps more precisely. We further propose a residual channel attention (RCA) module to emphasize the interdependence between feature maps and exploit the global channel correlation of them, which could enhance the channel-wise adaptive learning. In addition, multiscale joint spatial–spectral input and residual learning strategies are employed to capture multiscale spatial–spectral features and reduce the degradation problem, respectively. Synthetic and real HSI data experiments demonstrated that the proposed HSI denoising network outperforms many of the advanced methods in both quantitative and qualitative assessments. Full article
(This article belongs to the Special Issue Pattern Recognition in Hyperspectral Remote Sensing)
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Review

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27 pages, 3755 KiB  
Review
Hyperspectral Anomaly Detection Using Deep Learning: A Review
by Xing Hu, Chun Xie, Zhe Fan, Qianqian Duan, Dawei Zhang, Linhua Jiang, Xian Wei, Danfeng Hong, Guoqiang Li, Xinhua Zeng, Wenming Chen, Dongfang Wu and Jocelyn Chanussot
Remote Sens. 2022, 14(9), 1973; https://doi.org/10.3390/rs14091973 - 20 Apr 2022
Cited by 43 | Viewed by 7538
Abstract
Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots in the field of remote sensing. Because HSI’s features of integrating image and spectrum provide a considerable data basis for abnormal object detection, HSI-AD has a huge application potential in HSI analysis. [...] Read more.
Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots in the field of remote sensing. Because HSI’s features of integrating image and spectrum provide a considerable data basis for abnormal object detection, HSI-AD has a huge application potential in HSI analysis. It is difficult to effectively extract a large number of nonlinear features contained in HSI data using traditional machine learning methods, and deep learning has incomparable advantages in the extraction of nonlinear features. Therefore, deep learning has been widely used in HSI-AD and has shown excellent performance. This review systematically summarizes the related reference of HSI-AD based on deep learning and classifies the corresponding methods into performance comparisons. Specifically, we first introduce the characteristics of HSI-AD and the challenges faced by traditional methods and introduce the advantages of deep learning in dealing with these problems. Then, we systematically review and classify the corresponding methods of HSI-AD. Finally, the performance of the HSI-AD method based on deep learning is compared on several mainstream data sets, and the existing challenges are summarized. The main purpose of this article is to give a more comprehensive overview of the HSI-AD method to provide a reference for future research work. Full article
(This article belongs to the Special Issue Pattern Recognition in Hyperspectral Remote Sensing)
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