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Advances in Hyperspectral Remote Sensing: Methods and Applications

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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 December 2022) | Viewed by 32457

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Prof. Dr. Jiayi Ma

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Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: machine learning; computer vision; information fusion; image super resolution; hyperspectral image analysis; infrared imaging; image denoising
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Prof. Dr. Xin Tian

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Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: computational imaging; information fusion; hyperspectral imaging; image super-resolution; image denoising; single-photon imaging
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Prof. Dr. Jun Chen

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School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: pattern recognition; computer vision; image fusion; image stitching; image matching; geological hazard monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Benefiting from the development of hyperspectral satellites, hyperspectral imaging has played an important role in remote sensing. Hyperspectral images collect information with contiguous or noncontiguous 10 nm bands across the 400-2500 nm region of the electromagnetic spectrum. Thus, hyperspectral images with hundreds of spectral bands including abundant spectral information can be applied for the detection of minerals, environmental monitoring, and defense.

By combining imaging and spectroscopy in a system, hyperspectral images always have complex high-dimensional data structures, leading to the difficulty in processing and analyzing hyperspectral images with high efficiency. For example, the ill-posed problem in the recovery of high-dimensional data is more challenging than that of low-dimensional data. Although numerous efforts have been devoted to addressing special hyperspectral remote sensing tasks, complex datasets in real applications and difficult real-world tasks still put forward urgent requirements of advanced hyperspectral remote sensing methods. Further, the applications of hyperspectral remote sensing have been hot topics in recent years.

This Special Issue aims to gather high-level contributions related to advances in hyperspectral remote sensing. Both original research articles with innovative ideas and review articles discussing the state of the art are welcomed. Specific topics of interest include, but are not limited to, the following:

Hyperspectral image denoising, deblurring, unmixing, and restoration; image super-resolution, image fusion, and image segmentation; image quality assessment; classification, clustering, and object detection; deep learning technique in hyperspectral image processing; registration and feature selection; model-based approaches and tensor decomposition; change detection and compression; surveys of recent progress; application of hyperspectral remote sensing.

Prof. Dr. Jiayi Ma
Prof. Dr. Xin Tian
Prof. Dr. Jun Chen
Guest Editor

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Keywords

remote sensing
hyperspectral image
hyperspectral image denoise
hyperspectral image unmixing
hyperspectral image super-resolution
hyperspectral image fusion
hyperspectral image classification
image registration
change detection

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Related Special Issue

Advances in Hyperspectral Remote Sensing: Methods and Applications II in Remote Sensing

Published Papers (11 papers)

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Research

22 pages, 8800 KiB

Open AccessArticle

An Unmixing-Based Multi-Attention GAN for Unsupervised Hyperspectral and Multispectral Image Fusion

by Lijuan Su, Yuxiao Sui and Yan Yuan

Remote Sens. 2023, 15(4), 936; https://doi.org/10.3390/rs15040936 - 8 Feb 2023

Cited by 5 | Viewed by 2253

Abstract

Hyperspectral images (HSI) frequently have inadequate spatial resolution, which hinders numerous applications for the images. High resolution multispectral image (MSI) has been fused with HSI to reconstruct images with both high spatial and high spectral resolutions. In this paper, we propose a generative adversarial network (GAN)-based unsupervised HSI-MSI fusion network. In the generator, two coupled autoencoder nets decompose HSI and MSI into endmembers and abundances for fusing high resolution HSI through the linear mixing model. The two autoencoder nets are connected by a degradation-generation (DG) block, which further improves the accuracy of the reconstruction. Additionally, a coordinate multi-attention net (CMAN) is designed to extract more detailed features from the input. Driven by the joint loss function, the proposed method is straightforward and easy to execute in an end-to-end training manner. The experimental results demonstrate that the proposed strategy outperforms the state-of-art methods. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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22 pages, 10515 KiB

Open AccessArticle

Ore-Waste Discrimination Using Supervised and Unsupervised Classification of Hyperspectral Images

by Mehdi Abdolmaleki, Mariano Consens and Kamran Esmaeili

Remote Sens. 2022, 14(24), 6386; https://doi.org/10.3390/rs14246386 - 17 Dec 2022

Cited by 12 | Viewed by 3036

Abstract

Ore and waste discrimination is essential for optimizing exploitation and minimizing ore dilution in a mining operation. The conventional ore/waste discrimination approach relies on the interpretation of ore control by geologists, which is subjective, time-consuming, and can cause safety hazards. Hyperspectral remote sensing can be used as an alternative approach for ore/waste discrimination. The focus of this study is to investigate the application of hyperspectral remote sensing and deep learning (DL) for real-time ore and waste classification. Hyperspectral images of several meters of drill core samples from a silver ore deposit labeled by a site geologist as ore and waste material were used to train and test the models. A DL model was trained on the labels generated by a spectral angle mapper (SAM) machine learning technique. The performance on ore/waste discrimination of three classifiers (supervised DL and SAM, and unsupervised k-means clustering) was evaluated using Rand Error and Pixel Error as disagreement analysis and accuracy assessment indices. The results showed that the DL method outperformed the other two techniques. The performance of the DL model reached 0.89, 0.95, 0.89, and 0.91, respectively, on overall accuracy, precision, recall, and F1 score, which indicate the strong capability of the DL model in ore and waste discrimination. An integrated hyperspectral imaging and DL technique has strong potential to be used for practical and efficient discrimination of ore and waste in a near real-time manner. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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23 pages, 6180 KiB

Open AccessArticle

Detection of the New Class of Hypersonic Targets under Emerging Hyperspectral Sample Streams: An Unsupervised Isolation Forest Solution

by Shurong Yuan, Lei Shi, Bo Yao, Yutong Zhai, Fangyan Li and Yuefan Du

Remote Sens. 2022, 14(20), 5191; https://doi.org/10.3390/rs14205191 - 17 Oct 2022

Cited by 1 | Viewed by 1973

Abstract

Rapid detection of the new class of hypersonic targets (HTs) presenting unknown military threats in space-based surveillance will guarantee aerospace security. This paper proposes an unsupervised subclass definition and an efficient isolation forest based on an anomalous hyperspectral feature selection (USD-EiForest) algorithm to detect the new class of never-before-seen HTs under emerging hyperspectral sample streams. First, we reveal that the hyperspectral features (HFs) of the new class of HTs have no anomaly characteristics when compared to the globally observed samples while having prominent anomaly characteristics when compared to the subclasses of observed samples. Second, an unsupervised subclass definition method adapted to HTs is utilized to classify the observed samples into several subclasses. Then, an efficient isolation forest is designed to determine whether the data stream sample in each subclass indicates anomaly features that mark the detection of the new class of hypersonic targets (DNHT). Finally, we experiment on the simulated hyperspectral HTs data sets considering the RAM-C II HT as the observed samples and the HTV-2 HT as the unknown samples. The results suggest that the performance of our proposal has competitive advantages in terms of accuracy and detection efficiency. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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20 pages, 2406 KiB

Open AccessArticle

A Novel Knowledge Distillation Method for Self-Supervised Hyperspectral Image Classification

by Qiang Chi, Guohua Lv, Guixin Zhao and Xiangjun Dong

Remote Sens. 2022, 14(18), 4523; https://doi.org/10.3390/rs14184523 - 10 Sep 2022

Cited by 11 | Viewed by 2694

Abstract

Using deep learning to classify hyperspectral image(HSI) with only a few labeled samples available is a challenge. Recently, the knowledge distillation method based on soft label generation has been used to solve classification problems with a limited number of samples. Unlike normal labels, soft labels are considered the probability of a sample belonging to a certain category, and are therefore more informative for the sake of classification. The existing soft label generation methods for HSI classification cannot fully exploit the information of existing unlabeled samples. To solve this problem, we propose a novel self-supervised learning method with knowledge distillation for HSI classification, termed SSKD. The main motivation is to exploit more valuable information for classification by adaptively generating soft labels for unlabeled samples. First, similarity discrimination is performed using all unlabeled and labeled samples by considering both spatial distance and spectral distance. Then, an adaptive nearest neighbor matching strategy is performed for the generated data. Finally, probabilistic judgment for the category is performed to generate soft labels. Compared to the state-of-the-art method, our method improves the classification accuracy by 4.88%, 7.09% and 4.96% on three publicly available datasets, respectively. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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19 pages, 4934 KiB

Open AccessArticle

Dictionary Learning-Cooperated Matrix Decomposition for Hyperspectral Target Detection

by Yuan Yao, Mengbi Wang, Ganghui Fan, Wendi Liu, Yong Ma and Xiaoguang Mei

Remote Sens. 2022, 14(17), 4369; https://doi.org/10.3390/rs14174369 - 2 Sep 2022

Cited by 4 | Viewed by 1643

Abstract

Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed to promote the separation of the background and targets, but they suffer from two problems: (1) Targets are detected with the criterion of reconstruction residuals, and the imbalanced number of background and target atoms in union dictionary may lead to misclassification of targets. (2) The detection results are susceptible to the quality of the

a p r i o r i

target spectra, thus obtaining inferior performance because of the inevitable spectral variability. In this paper, we propose a matrix decomposition-based detector named dictionary learning-cooperated matrix decomposition (DLcMD) for hyperspectral target detection. The procedure of DLcMD is two-fold. First, the low rank and sparse matrix decomposition (LRaSMD) is exploited to separate targets from the background due to its insensitivity to the imbalanced number of background and target atoms, which can reduce the misclassification of targets. Inspired by dictionary learning, the target atoms are updated during LRaSMD to alleviate the impact of spectral variability. After that, a binary hypothesis model specifically designed for LRaSMD is proposed, and a generalized likelihood ratio test (GLRT) is performed to obtain the final detection result. Experimental results on five datasets have shown the reliability of the proposed method. Especially in the Los Angeles-II dataset, the area under the curve (AUC) value is nearly 16% higher than the average value of the other seven detectors, which reveals the superiority of DLcMD in hyperspectral target detection. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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20 pages, 10852 KiB

Open AccessArticle

Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

by Yujie Zhang, Xiaoguang Mei, Yong Ma, Xingyu Jiang, Zongyi Peng and Jun Huang

Remote Sens. 2022, 14(16), 4038; https://doi.org/10.3390/rs14164038 - 18 Aug 2022

Cited by 13 | Viewed by 3313

Abstract

Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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18 pages, 16272 KiB

Open AccessArticle

ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing

by Xintong Zhang, Aiwu Zhang, Raechel Portelli, Xizhen Zhang and Hongliang Guan

Remote Sens. 2022, 14(16), 4034; https://doi.org/10.3390/rs14164034 - 18 Aug 2022

Cited by 1 | Viewed by 2591

Abstract

This paper proposes an endmember matrix constraint unmixing method for ZY-1 02D hyperspectral imagery (HSI) super-resolution reconstruction (SRR) to overcome the low resolution of ZY-1 02D HSI. The proposed method combines spectral unmixing and adds novel smoothing constraints to traditional non-negative matrix factorization to improve details and preserve the spectral information of traditional SRR methods. The full utilization of the endmember spectral matrix and endmember abundance matrix of HSI and multispectral imagery (MSI) reconstructs the high spatial resolution and high spectral fidelity HSI. Furthermore, given the ZY-1 02D HSI infrared bands are seriously corrupted by noise, the influence of denoising on the SRR accuracy is also discussed. Experiments show that the proposed method restores spatial details and spectral information and is robust for noise, preserving more spectral information. Therefore, the proposed method is a ZY-1 02D HSI SRR method with high spatial resolution and high spectral fidelity, which improves the spatial resolution while simultaneously solving spectral mixing and provides the possibility for the data further expansion. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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18 pages, 1702 KiB

Open AccessArticle

Reliable Label-Supervised Pixel Attention Mechanism for Weakly Supervised Building Segmentation in UAV Imagery

by Jun Chen, Weifeng Xu, Yang Yu, Chengli Peng and Wenping Gong

Remote Sens. 2022, 14(13), 3196; https://doi.org/10.3390/rs14133196 - 3 Jul 2022

Cited by 3 | Viewed by 1981

Abstract

Building segmentation for Unmanned Aerial Vehicle (UAV) imagery usually requires pixel-level labels, which are time-consuming and expensive to collect. Weakly supervised semantic segmentation methods for image-level labeling have recently achieved promising performance in natural scenes, but there have been few studies on UAV remote sensing imagery. In this paper, we propose a reliable label-supervised pixel attention mechanism for building segmentation in UAV imagery. Our method is based on the class activation map. However, classification networks tend to capture discriminative parts of the object and are insensitive to over-activation; therefore, class activation maps cannot directly guide segmentation network training. To overcome these challenges, we first design a Pixel Attention Module that captures rich contextual relationships, which can further mine more discriminative regions, in order to obtain a modified class activation map. Then, we use the initial seeds generated by the classification network to synthesize reliable labels. Finally, we design a reliable label loss, which is defined as the sum of the pixel-level differences between the reliable labels and the modified class activation map. Notably, the reliable label loss can handle over-activation. The preceding steps can significantly improve the quality of the pseudo-labels. Experiments on our home-made UAV data set indicate that our method can achieve 88.8% mIoU on the test set, outperforming previous state-of-the-art weakly supervised methods. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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21 pages, 54881 KiB

Open AccessArticle

Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency

by Xi Gong, Feng Yao, Jiayi Ma, Junjun Jiang, Tao Lu, Yanduo Zhang and Huabing Zhou

Remote Sens. 2022, 14(11), 2606; https://doi.org/10.3390/rs14112606 - 29 May 2022

Cited by 13 | Viewed by 3364

Abstract

Feature matching is a key method of feature-based image registration, which refers to establishing reliable correspondence between feature points extracted from two images. In order to eliminate false matchings from the initial matchings, we propose a simple and efficient method. The key principle of our method is to maintain the topological and affine transformation consistency among the neighborhood matches. We formulate this problem as a mathematical model and derive a closed solution with linear time and space complexity. More specifically, our method can remove mismatches from thousands of hypothetical correspondences within a few milliseconds. We conduct qualitative and quantitative experiments on our method on different types of remote-sensing datasets. The experimental results show that our method is general, and it can deal with all kinds of remote-sensing image pairs, whether rigid or non-rigid image deformation or image pairs with various shadow, projection distortion, noise, and geometric distortion. Furthermore, it is two orders of magnitude faster and more accurate than state-of-the-art methods and can be used for real-time applications. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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27 pages, 2407 KiB

Open AccessArticle

Cross-Modal Feature Representation Learning and Label Graph Mining in a Residual Multi-Attentional CNN-LSTM Network for Multi-Label Aerial Scene Classification

by Peng Li, Peng Chen and Dezheng Zhang

Remote Sens. 2022, 14(10), 2424; https://doi.org/10.3390/rs14102424 - 18 May 2022

Cited by 12 | Viewed by 2777

Abstract

The results of aerial scene classification can provide valuable information for urban planning and land monitoring. In this specific field, there are always a number of object-level semantic classes in big remote-sensing pictures. Complex label-space makes it hard to detect all the targets and perceive corresponding semantics in the typical scene, thereby weakening the sensing ability. Even worse, the preparation of a labeled dataset for the training of deep networks is more difficult due to multiple labels. In order to mine object-level visual features and make good use of label dependency, we propose a novel framework in this article, namely a Cross-Modal Representation Learning and Label Graph Mining-based Residual Multi-Attentional CNN-LSTM framework (CM-GM framework). In this framework, a residual multi-attentional convolutional neural network is developed to extract object-level image features. Moreover, semantic labels are embedded by language model and then form a label graph which can be further mapped by advanced graph convolutional networks (GCN). With these cross-modal feature representations (image, graph and text), object-level visual features will be enhanced and aligned to GCN-based label embeddings. After that, aligned visual signals are fed into a bi-LSTM subnetwork according to the built label graph. The CM-GM framework is able to map both visual features and graph-based label representations into a correlated space appropriately, using label dependency efficiently, thus improving the LSTM predictor’s ability. Experimental results show that the proposed CM-GM framework is able to achieve higher accuracy on many multi-label benchmark datasets in remote sensing field. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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20 pages, 32290 KiB

Open AccessArticle

UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

by Jun Chen, Zixian Li, Chengli Peng, Yong Wang and Wenping Gong

Remote Sens. 2022, 14(5), 1068; https://doi.org/10.3390/rs14051068 - 22 Feb 2022

Cited by 20 | Viewed by 4240

Abstract

This paper introduces an Unmanned Aerial Vehicle (UAV) image stitching method, based on the optimal seam algorithm and half-projective warp, that can effectively retain the original information of the image and obtain the ideal stitching effect. The existing seam stitching algorithms can eliminate the ghosting and blurring problems on the stitched images, but the deformation and angle distortion caused by image registration will remain in the stitching results. To overcome this situation, we propose a stitching strategy based on optimal seam and half-projective warp. Firstly, we define a new difference matrix in the overlapping region of the aligned image, which includes the color, structural and line difference information. Then, we constrain the search range of the seam by the minimum energy, and propose a seam search algorithm based on the global minimum energy to obtain the seam. Finally, combined with the seam position and half-projective warp, the shape of the stitched image is rectified to keep more regions in their original shape. The experimental results of several groups of UAV images show that our method has a superior stitching effect. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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