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Remote Sensing
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Learning-Based Hyperspectral Information Extraction: Algorithms and Applications
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Learning-Based Hyperspectral Information Extraction: Algorithms and Applications

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 62985

Special Issue Editors

Prof. Dr. Danfeng Hong

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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
Interests: remote sensing; hyperspectral image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Lianru Gao

E-Mail Website
Guest Editor
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: geophysical image processing; image classification; hyperspectral imaging; remote sensing; feature extraction; image resolution; learning (artificial intelligence); geophysical techniques; object detection; feedforward neural nets; optical radar; convolutional neural nets; image fusion; image reconstruction; image representation; remote sensing by laser beam; Bayes methods; Markov processes; aerosols; agriculture; air pollution; artificial satellites; atmospheric optics; convex programming; convolution
Dr. Xiuping Jia

E-Mail Website
Guest Editor
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
Interests: photogrammetry and remote sensing; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Hairong Qi

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996-2100, USA
Interests: image processing; computer vision; machine learning; collaborative information processing in sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Hyperspectral images (HSI) possess very rich and diverse spectral information with hundreds of contiguous bands, that largely facilitate the detection and recognition of materials at a more accurate level. However, their high dimensionality also introduces some drawbacks, including, for example, data redundancy and more complex noise. In addition, due to hardware limitations, its spatial resolution is significantly lower than that of multispectral images (MSI) with around 10 spectral bands or conventional color images (RGB with only 3 bands). These potential issues have inevitably led to unique challenges in extracting useful information from HSI. Hence, it is of paramount importance to exploit the subject of advanced and intelligent HSI analysis algorithms to extract robust discriminative and representative information in a more effective and automatic fashion. This Special Issue focuses on learning-based information extraction that is mostly data-driven.

Potential topics for this Special Issue include, but are not limited to the following:

  • Data-driven intelligent algorithms for low-level hyperspectral vision tasks, such as restoration, dimensionality reduction, endmember estimation, spectral unmixing, etc.
  • Feature extraction methods for various high-level applications in hyperspectral imaging, i.e., classification, target detection, object detection and tracking, time-series analysis, image retrieval, etc.
  • Advanced machine learning methods for model-free information extraction, including unsupervised, supervised, semi-supervised, weakly-supervised, and self-supervised learning approaches.
  • Efficient neural architecture search, e.g., AutoML, and meta-learning strategy for effective and automatic hyperspectral information extraction.
  • Hyperspectral super-resolution.
  • Transfer learning including quantitative parameter inversion (e.g., vegetation, water body, and soil).
  • Intelligent hyperspectral information processing tools or systems.
  • Novel methods and evaluation tools and the construction of benchmark datasets.

Dr. Danfeng Hong
Prof. Lianru Gao
Prof. Xiuping Jia
Prof. Hairong Qi
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.

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Published Papers (8 papers)

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Research

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19 pages, 8877 KiB  
Article
Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
by Qin Jiang, Yifei Dong, Jiangtao Peng, Mei Yan and Yi Sun
Remote Sens. 2021, 13(13), 2637; https://doi.org/10.3390/rs13132637 - 5 Jul 2021
Cited by 5 | Viewed by 2686
Abstract
Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. [...] Read more.
Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. Because the objective function of NMF is the traditional least-squares function, NMF is sensitive to noise. In order to improve the robustness of NMF, this paper proposes a maximum likelihood estimation (MLE) based NMF model (MLENMF) for unmixing of hyperspectral images (HSIs), which substitutes the least-squares objective function in traditional NMF by a robust MLE-based loss function. Experimental results on a simulated and two widely used real hyperspectral data sets demonstrate the superiority of our MLENMF over existing NMF methods. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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20 pages, 8426 KiB  
Article
Hyperspectral Image Classification with Localized Graph Convolutional Filtering
by Shengliang Pu, Yuanfeng Wu, Xu Sun and Xiaotong Sun
Remote Sens. 2021, 13(3), 526; https://doi.org/10.3390/rs13030526 - 2 Feb 2021
Cited by 18 | Viewed by 4842
Abstract
The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be [...] Read more.
The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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21 pages, 13778 KiB  
Article
Spatial-Spectral Transformer for Hyperspectral Image Classification
by Xin He, Yushi Chen and Zhouhan Lin
Remote Sens. 2021, 13(3), 498; https://doi.org/10.3390/rs13030498 - 30 Jan 2021
Cited by 270 | Viewed by 13667
Abstract
Recently, a great many deep convolutional neural network (CNN)-based methods have been proposed for hyperspectral image (HSI) classification. Although the proposed CNN-based methods have the advantages of spatial feature extraction, they are difficult to handle the sequential data with and CNNs are not [...] Read more.
Recently, a great many deep convolutional neural network (CNN)-based methods have been proposed for hyperspectral image (HSI) classification. Although the proposed CNN-based methods have the advantages of spatial feature extraction, they are difficult to handle the sequential data with and CNNs are not good at modeling the long-range dependencies. However, the spectra of HSI are a kind of sequential data, and HSI usually contains hundreds of bands. Therefore, it is difficult for CNNs to handle HSI processing well. On the other hand, the Transformer model, which is based on an attention mechanism, has proved its advantages in processing sequential data. To address the issue of capturing relationships of sequential spectra in HSI in a long distance, in this study, Transformer is investigated for HSI classification. Specifically, in this study, a new classification framework titled spatial-spectral Transformer (SST) is proposed for HSI classification. In the proposed SST, a well-designed CNN is used to extract the spatial features, and a modified Transformer (a Transformer with dense connection, i.e., DenseTransformer) is proposed to capture sequential spectra relationships, and multilayer perceptron is used to finish the final classification task. Furthermore, dynamic feature augmentation, which aims to alleviate the overfitting problem and therefore generalize the model well, is proposed and added to the SST (SST-FA). In addition, to address the issue of limited training samples in HSI classification, transfer learning is combined with SST, and another classification framework titled transferring-SST (T-SST) is proposed. At last, to mitigate the overfitting problem and improve the classification accuracy, label smoothing is introduced for the T-SST-based classification framework (T-SST-L). The proposed SST, SST-FA, T-SST, and T-SST-L are tested on three widely used hyperspectral datasets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the concept of Transformer opens a new window for HSI classification. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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19 pages, 1748 KiB  
Article
Multiscale Adjacent Superpixel-Based Extended Multi-Attribute Profiles Embedded Multiple Kernel Learning Method for Hyperspectral Classification
by Lei Pan, Chengxun He, Yang Xiang and Le Sun
Remote Sens. 2021, 13(1), 50; https://doi.org/10.3390/rs13010050 - 25 Dec 2020
Cited by 5 | Viewed by 2791
Abstract
In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral image (HSI) classification. First, the original HSI is reduced [...] Read more.
In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral image (HSI) classification. First, the original HSI is reduced to three principal components in the spectral domain using principal component analysis (PCA). Then, a fast and efficient segmentation algorithm named simple linear iterative clustering is utilized to segment the principal components into a certain number of superpixels. By setting different numbers of superpixels, a set of multiscale homogenous regional features is extracted. Based on those extracted superpixels and their first-order adjacent superpixels, EMAPs with multimodal features are extracted and embedded into the multiple kernel framework to generate different spatial and spectral kernels. Finally, a PCA-based kernel learning algorithm is used to learn an optimal kernel that contains multiscale and multimodal information. The experimental results on two well-known datasets validate the effectiveness and efficiency of the proposed method compared with several state-of-the-art HSI classifiers. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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19 pages, 6525 KiB  
Article
Learning-Based Hyperspectral Imagery Compression through Generative Neural Networks
by Chubo Deng, Yi Cen and Lifu Zhang
Remote Sens. 2020, 12(21), 3657; https://doi.org/10.3390/rs12213657 - 8 Nov 2020
Cited by 16 | Viewed by 3265
Abstract
Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, [...] Read more.
Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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20 pages, 7365 KiB  
Article
Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing
by Xiaoling Deng, Zihao Zhu, Jiacheng Yang, Zheng Zheng, Zixiao Huang, Xianbo Yin, Shujin Wei and Yubin Lan
Remote Sens. 2020, 12(17), 2678; https://doi.org/10.3390/rs12172678 - 19 Aug 2020
Cited by 56 | Viewed by 6891
Abstract
Citrus is an important cash crop in the world, and huanglongbing (HLB) is a destructive disease in the citrus industry. To efficiently detect the degree of HLB stress on large-scale orchard citrus trees, an UAV (Uncrewed Aerial Vehicle) hyperspectral remote sensing tool is [...] Read more.
Citrus is an important cash crop in the world, and huanglongbing (HLB) is a destructive disease in the citrus industry. To efficiently detect the degree of HLB stress on large-scale orchard citrus trees, an UAV (Uncrewed Aerial Vehicle) hyperspectral remote sensing tool is used for HLB rapid detection. A Cubert S185 (Airborne Hyperspectral camera) was mounted on the UAV of DJI Matrice 600 Pro to capture the hyperspectral remote sensing images; and a ASD Handheld2 (spectrometer) was used to verify the effectiveness of the remote sensing data. Correlation-proven UAV hyperspectral remote sensing data were used, and canopy spectral samples based on single pixels were extracted for processing and analysis. The feature bands extracted by the genetic algorithm (GA) of the improved selection operator were 468 nm, 504 nm, 512 nm, 516 nm, 528 nm, 536 nm, 632 nm, 680 nm, 688 nm, and 852 nm for the HLB detection. The proposed HLB detection methods (based on the multi-feature fusion of vegetation index) and canopy spectral feature parameters constructed (based on the feature band in stacked autoencoder (SAE) neural network) have a classification accuracy of 99.33% and a loss of 0.0783 for the training set, and a classification accuracy of 99.72% and a loss of 0.0585 for the validation set. This performance is higher than that based on the full-band AutoEncoder neural network. The field-testing results show that the model could effectively detect the HLB plants and output the distribution of the disease in the canopy, thus judging the plant disease level in a large area efficiently. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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22 pages, 11657 KiB  
Article
PercepPan: Towards Unsupervised Pan-Sharpening Based on Perceptual Loss
by Changsheng Zhou, Jiangshe Zhang, Junmin Liu, Chunxia Zhang, Rongrong Fei and Shuang Xu
Remote Sens. 2020, 12(14), 2318; https://doi.org/10.3390/rs12142318 - 19 Jul 2020
Cited by 35 | Viewed by 4058
Abstract
In the literature of pan-sharpening based on neural networks, high resolution multispectral images as ground-truth labels generally are unavailable. To tackle the issue, a common method is to degrade original images into a lower resolution space for supervised training under the Wald’s protocol. [...] Read more.
In the literature of pan-sharpening based on neural networks, high resolution multispectral images as ground-truth labels generally are unavailable. To tackle the issue, a common method is to degrade original images into a lower resolution space for supervised training under the Wald’s protocol. In this paper, we propose an unsupervised pan-sharpening framework, referred to as “perceptual pan-sharpening”. This novel method is based on auto-encoder and perceptual loss, and it does not need the degradation step for training. For performance boosting, we also suggest a novel training paradigm, called “first supervised pre-training and then unsupervised fine-tuning”, to train the unsupervised framework. Experiments on the QuickBird dataset show that the framework with different generator architectures could get comparable results with the traditional supervised counterpart, and the novel training paradigm performs better than random initialization. When generalizing to the IKONOS dataset, the unsupervised framework could still get competitive results over the supervised ones. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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Review

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32 pages, 1205 KiB  
Review
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
by Ava Vali, Sara Comai and Matteo Matteucci
Remote Sens. 2020, 12(15), 2495; https://doi.org/10.3390/rs12152495 - 3 Aug 2020
Cited by 251 | Viewed by 22896
Abstract
Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the [...] Read more.
Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field. Full article
(This article belongs to the Special Issue Learning-Based Hyperspectral Information Extraction: Algorithms and Applications)
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