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Hyperspectral Remote Sensing Imaging and Processing

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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 (31 October 2023) | Viewed by 11353

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Prof. Dr. Xiangtao Zheng

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Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
Interests: remote sensing scene classification; cross-domain scene classification
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Prof. Dr. Yanfeng Gu

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School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: space intelligent remote sensing; multi-mode hyperspectral remote sensing; intelligent application of remote sensing big data
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Prof. Dr. Geng Zhang

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Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
Interests: hyperspectral image processing; pattern recognition

Dr. Benoit Vozel

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Institute of Electronics and Telecommunications IETR UMR CNRS 6164, University of Rennes, 22305 Lannion, France
Interests: blind estimation of degradation characteristics (noise, PSF); blind restoration of multicomponent images; multimodal image correction; multicomponent image compression; multi-channel adaptive processing of signals and images; unsupervised machine learning and deep learning; multi-mode remote sensing data processing; remote sensing
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Dear Colleagues,

Hyperspectral images can record a spectra of scenes in different electromagnetic wavelengths, which have been widely used in precision agriculture, environmental monitoring, and national defense. With the rapid progress of spectral imaging technology, temporal, spatial, and spectral resolutions have been improved. Furthermore, different remote sensing data can provide complementary characteristics for spectral imaging. Multi-source data have emerged, such as panchromatic images (PANs), multispectral images (MSIs), synthetic aperture radar (SAR), and light detection and ranging (LiDAR). However, it is difficult to handle these high-resolution hyperspectral images due to computational complexity and heterogeneous scenarios. To address the applicable problem, the characteristics of hyperspectral images should be extensively explored to develop novel theories and methods.

Therefore, this Special Issue aims to investigate latest advances and trends of hyperspectral remote sensing. Topics of interest include but are not limited to the following:

Hyperspectral denoising and enhancement;
Hyperspectral super-resolution and spectral unmixing;
Hyperspectral classification, segmentation, and retrieval；
Hyperspectral data fusion and compression；
Hyperspectral detection and tracking；
Hyperspectral band selection and feature extraction;
Multi-temporal hyperspectral analysis;
Hyperspectral applications: earth observation, precision agriculture, forest environmental monitoring, and disaster monitoring.

Dr. Xiangtao Zheng
Prof. Dr. Yanfeng Gu
Prof. Dr. Geng Zhang
Dr. Benoit Vozel
Guest Editors

Manuscript Submission Information

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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

spectral imaging
hyperspectral image processing
data fusion
change detection
machine learning
feature extraction
target detection and identification

Published Papers (8 papers)

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22 pages, 7672 KiB

Open AccessArticle

Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising

by Jie Han, Chuang Pan, Haiyong Ding and Zhichao Zhang

Remote Sens. 2024, 16(1), 109; https://doi.org/10.3390/rs16010109 - 26 Dec 2023

Viewed by 746

Abstract

Hyperspectral image (HSIs) denoising is a preprocessing step that plays a crucial role in many applications used in Earth observation missions. Low-rank tensor representation can be utilized to restore mixed-noise HSIs, such as those affected by mixed Gaussian, impulse, stripe, and deadline noises. Although there is a considerable body of research on spatial and spectral prior knowledge concerning subspace, the correlation between the spectral continuity and the nonlocal sparsity of the spectral and spatial factors is not yet fully understood. To address this deficiency, in the present study, we determined the correlation between these factors using a cascaded technique, and we describe in this paper the double-factor tensor cascaded-rank (DFTCR) minimization method that was used. The information existing in the nonlocal sparsity property of the spatial factor was employed to promote a geometrical feature representation, and a tensor cascaded-rank minimization approach was introduced as a nonlocal self-similarity to promote restoration quality. The continuity between the difference and nonlocal gradient sparsity constraints of the spectral factor was also introduced to learn the basis. Furthermore, to estimate the solutions of the proposed model, we developed an algorithm based on the alternating direction method of multipliers (ADMM). The performance of the DFTCR method was tested by a comparison with eleven established denoising methods for HSIs. The results showed that the proposed DFTCR method exhibited superior performance in the removal of mixed noise from HSIs. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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21 pages, 6503 KiB

Open AccessArticle

A Novel Method Based on GPU for Real-Time Anomaly Detection in Airborne Push-Broom Hyperspectral Sensors

by Tianru Xue, Chongru Wang, Hui Xie and Yueming Wang

Remote Sens. 2023, 15(18), 4449; https://doi.org/10.3390/rs15184449 - 10 Sep 2023

Viewed by 894

Abstract

The airborne hyperspectral remote sensing systems (AHRSSs) acquire images with high spectral resolution, high spatial resolution, and high temporal dimension. While the AHRSS captures more detailed information from the terrain objects, the computational complexity of data processing is greatly increased. As an important application technology in the hyperspectral domain, anomaly detection (AD) processing must be real-time and high-precision in many cases, such as post-disaster rescue, military battlefield search, and natural disaster detection. In this paper, the real-time AD technology for the push-broom AHRSS is studied, the mathematical model is established, and a novel implementation framework is proposed. Firstly, the optimized kernel minimum noise fraction (OP-KMNF) transformation is employed to extract informative and discriminative features between the background and anomalies. Secondly, the Nyström method is introduced to reduce the computational complexity of OP-KMNF transformation by decomposing and extrapolating the sub-kernel matrix to estimate the eigenvector of the entire kernel matrix. Thirdly, the extracted features are transferred to hard disks for data storage. Then, taking the extracted features as input data, the background separation model-based CEM anomaly detector (BSM-CEMAD) is imported to detect anomalies. Finally, graphics processing unit (GPU) parallel computing is utilized in the Nyström-based OP-KMNF (NOP-KMNF) transformation and the BSM-CEMAD to improve the execution efficiency, and the real-time AD for the push-broom AHRSS could be realized. To test the feasibility of the implementation framework proposed in this paper, the experiment is carried out with the Airborne Multi-Modular Imaging Spectrometer (AMMIS) developed by the Shanghai Institute of Technical Physics as the data acquisition platform. The experimental results show that the proposed method outperforms many other state-of-the-art AD methods in anomalies detection and background suppression. Moreover, under the condition that the downlink data could retain most of the hyperspectral data information, the proposed method achieves real-time detection of pixel-level anomalies, with the initial delay not exceeding 1 s, the false alarm rate (FAR) less than 5%, and the true positive rate (TPR) close to 98%. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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21 pages, 7694 KiB

Open AccessArticle

A Collaborative Superpixelwise Autoencoder for Unsupervised Dimension Reduction in Hyperspectral Images

by Chao Yao, Lingfeng Zheng, Longchao Feng, Fan Yang, Zehua Guo and Miao Ma

Remote Sens. 2023, 15(17), 4211; https://doi.org/10.3390/rs15174211 - 27 Aug 2023

Viewed by 851

Abstract

The dimension reduction (DR) technique plays an important role in hyperspectral image (HSI) processing. Among various DR methods, superpixel-based approaches offer flexibility in capturing spectral–spatial information and have shown great potential in HSI tasks. The superpixel-based methods divide the samples into groups and apply the DR technique to the small groups. Nevertheless, we find these methods would increase the intra-class disparity by neglecting the fact the samples from the same class may reside on different superpixels, resulting in performance decay. To address this problem, a novel unsupervised DR named the Collaborative superpixelwise Auto-Encoder (ColAE) is proposed in this paper. The ColAE begins by segmenting the HSI into different homogeneous regions using a superpixel-based method. Then, a set of Auto-Encoders (AEs) is applied to the samples within each superpixel. To reduce the intra-class disparity, a manifold loss is introduced to restrict the samples from the same class, even if located in different superpixels, to have similar representations in the code space. In this way, the compact and discriminative spectral–spatial feature is obtained. Experimental results on three HSI data sets demonstrate the promising performance of ColAE compared to existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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19 pages, 5996 KiB

Open AccessArticle

Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

by Lijing Bu, Dong Dai, Zhengpeng Zhang, Yin Yang and Mingjun Deng

Remote Sens. 2023, 15(13), 3225; https://doi.org/10.3390/rs15133225 - 21 Jun 2023

Cited by 4 | Viewed by 1422

Abstract

Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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19 pages, 5283 KiB

Open AccessArticle

Unsupervised Transformer Boundary Autoencoder Network for Hyperspectral Image Change Detection

by Song Liu, Haiwei Li, Feifei Wang, Junyu Chen, Geng Zhang, Liyao Song and Bingliang Hu

Remote Sens. 2023, 15(7), 1868; https://doi.org/10.3390/rs15071868 - 31 Mar 2023

Cited by 3 | Viewed by 1645

Abstract

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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19 pages, 3522 KiB

Open AccessArticle

SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection

by Zhenhua Mu, Ming Wang, Yihan Wang, Ruoxi Song and Xianghai Wang

Remote Sens. 2023, 15(3), 612; https://doi.org/10.3390/rs15030612 - 20 Jan 2023

Cited by 1 | Viewed by 1365

Abstract

Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral information divergence isolation double forest (SI2FM) in this paper. Further, the method excavates the intrinsic deep correlation properties between NSST subband coefficients of HSI in two ways to provide synergistic constraints and guidance on the prediction of abnormal target coefficients. On the one hand, with the “difference band” as a guide, the global isolation forest and local isolation forest models are constructed based on the spectral information divergence (SID) attribute values of the difference band and the low-frequency and high-frequency subbands, and the anomaly scores are determined by evaluating the path lengths of the isolation binary tree nodes in the forest model to obtain a progressively optimized anomaly detection map. On the other hand, based on the relationship of NSST high-frequency subband coefficients of spatial-spectral dimensions, the three-dimensional forest structure is constructed to realize the co-optimization of multiple anomaly detection maps obtained from the isolation forest. Finally, the guidance of the difference band suppresses the background noise and anomaly interference to a certain extent, enhancing the separability of target and background. The two-branch collaborative optimization based on the NSST subband coefficient correlation mining of HSI enables the prediction of anomaly sample coefficients to be gradually improved from multiple perspectives, which effectively improves the accuracy of anomaly detection. The effectiveness of the algorithm is verified by comparing real hyperspectral datasets captured in four different scenes with eleven typical anomaly detection algorithms currently available. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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19 pages, 12021 KiB

Open AccessArticle

Mid-Wave Infrared Snapshot Compressive Spectral Imager with Deep Infrared Denoising Prior

by Shuowen Yang, Hanlin Qin, Xiang Yan, Shuai Yuan and Qingjie Zeng

Remote Sens. 2023, 15(1), 280; https://doi.org/10.3390/rs15010280 - 03 Jan 2023

Cited by 1 | Viewed by 2034

Abstract

Although various infrared imaging spectrometers have been studied, most of them are developed under the Nyquist sampling theorem, which severely burdens 3D data acquisition, storage, transmission, and processing, in terms of both hardware and software. Recently, computational imaging, which avoids direct imaging, has been investigated for its potential in the visible field. However, it has been rarely studied in the infrared domain, as it suffers from inconsistency in spectral response and reconstruction. To address this, we propose a novel mid-wave infrared snapshot compressive spectral imager (MWIR-SCSI). This design scheme provides a high degree of randomness in the measurement projection, which is more conducive to the reconstruction of image information and makes spectral correction implementable. Furthermore, leveraging the explainability of model-based algorithms and the high efficiency of deep learning algorithms, we designed a deep infrared denoising prior plug-in for the optimization algorithm to perform in terms of both imaging quality and reconstruction speed. The system calibration obtains 111 real coded masks, filling the gap between theory and practice. Experimental results on simulation datasets and real infrared scenarios prove the efficacy of the designed deep infrared denoising prior plug-in and the proposed acquisition architecture that acquires mid-infrared spectral images of 640 pixels × 512 pixels × 111 spectral channels at an acquisition frame rate of 50 fps. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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17 pages, 4290 KiB

Open AccessTechnical Note

TRP-Oriented Hyperspectral Remote Sensing Image Classification Using Entropy-Weighted Ensemble Algorithm

by Shuhan Jia, Yu Li, Quanhua Zhao and Changqiang Wang

Remote Sens. 2023, 15(9), 2315; https://doi.org/10.3390/rs15092315 - 27 Apr 2023

Viewed by 932

Abstract

The problem that the randomly generated random projection matrix will lead to unstable classification results is addressed in this paper. To this end, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. In particular, this paper presents a random projection matrix selection strategy based on the separable information of a single class able to project the features of a certain class of objects. The projection result is measured by the degree of separability, thereby obtaining the low-dimensional image with optimal separability of the class. After projecting samples with the same random projection matrix, to calculate the distance matrix, the Minimum Distance classifier is devised, repeating for all classes. Finally, the weight of the distance matrix is considered in ensemble classification by using the information entropy. The proposed algorithm is tested on real hyperspectral remote sensing images. The experiments show an increase in both stability and performance. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)

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