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Deep Neural Networks for Hyperspectral Remote Sensing Image 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: 15 January 2025 | Viewed by 21097

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Special Issue Editors

Dr. Yulei Wang

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Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College Dalian Maritime University, Dalian, China
Interests: hyperspectral image processing; multi-source remote sensing image fusion; artificial intelligence
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Prof. Dr. Meiping Song

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Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: hyperspectral image processing; artificial intelligence; remote sensing
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Dr. Enyu Zhao

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Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: thermal infrared; hyperspectral; quantitative remote sensing

Dr. Weiying Xie

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State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: hyperspectral anomaly detection; network compression; efficient distributed learning
Special Issues, Collections and Topics in MDPI journals

Dr. Chunyan Yu

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Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: remote sensing image processing
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Prof. Dr. Caixia Gao

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: thermal infrared; hyperspectral; quantitative remote sensing

Prof. Dr. Silvia Liberata Ullo

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Department of Engineering (DING), University of Sannio, Benevento, Italy
Interests: signal processing; data and image analysis; satellite remote sensing; sensor networks; machine learning applied to satellite images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A hyperspectral image (HSI) is a three-dimensional cube containing rich spatial and spectral information with hundreds of narrow and contiguous wavebands generated by an imaging spectrometer. Each pixel in hyperspectral remote sensing images corresponds to a nearly continuous spectral curve, which can reflect substances’ diagnostic spectral absorption differences and provide rich spectral information for an accurate extraction of ground object information. Thanks to its high spectral resolution, hyperspectral images have received reasonable attention and have essential applications in military and civil fields. In recent years, with the continuous improvement of the hyperspectral data acquisition capability of satellites and aerial platforms, hyperspectral image processing has also developed towards big data-driven feature information extraction. However, processing the massive data collected by these platforms using traditional image analysis methodologies is impractical and ineffective. This calls for the adoption of powerful techniques that can extract reliable and useful information, where deep neural networks have been gradually applied in HSI processing due to the strong generalization and deep extraction properties of advanced semantic features.

This Special Issue aims to explore features that truly benefit hyperspectral remote sensing interpretation tasks and provides a forum for many individuals working in deep-learning-based hyperspectral image processing to report their research findings and share their experiences with the HSI community. All contributions to deep neural networks for hyperspectral remote sensing image processing are welcome to this Special Issue. Topics of interest include, but are not limited to, the following:

Deep neural networks for target detection, band selection, and classification in hyperspectral images.
Deep learning for surface parameters retrieval from thermal infrared images.
Deep feature extraction for multi-source remote sensing images.
The hybrid architecture of CNN and transformer for hyperspectral applications.
Feature fusion and learning for hyperspectral image processing.
Light-weight design of deep models.
Review/surveys of recent applications and techniques of hyperspectral images.

Dr. Yulei Wang
Prof. Dr. Meiping Song
Dr. Enyu Zhao
Dr. Weiying Xie
Dr. Chunyan Yu
Prof. Dr. Caixia Gao
Prof. Dr. Silvia Liberata Ullo
Guest Editors

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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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Keywords

deep neural networks
deep feature extraction
light weighed model
hyperspectral remote sensing images
thermal infrared
quantitative remote sensing
multi-sensor and multi-platform analysis
remote sensing applications

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Published Papers (13 papers)

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24 pages, 8893 KiB

Open AccessArticle

Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery

by Wenge Ni-Meister, Anthony Albanese and Francesca Lingo

Remote Sens. 2024, 16(17), 3313; https://doi.org/10.3390/rs16173313 - 6 Sep 2024

Viewed by 1066

Abstract

Tree species classification using hyperspectral imagery shows incredible promise in developing a large-scale, high-resolution model for identifying tree species, providing unprecedented details on global tree species distribution. Many questions remain unanswered about the best practices for creating a global, general hyperspectral tree species classification model. This study aims to address three key issues in creating a hyperspectral species classification model. We assessed the effectiveness of three data-labeling methods to create training data, three data-splitting methods for training/validation/testing, and machine-learning and deep-learning (including semi-supervised deep-learning) models for tree species classification using hyperspectral imagery at National Ecological Observatory Network (NEON) Sites. Our analysis revealed that the existing data-labeling method using the field vegetation structure survey performed reasonably well. The random tree data-splitting technique was the most efficient method for both intra-site and inter-site classifications to overcome the impact of spatial autocorrelation to avoid the potential to create a locally overfit model. Deep learning consistently outperformed random forest classification; both semi-supervised and supervised deep-learning models displayed the most promising results in creating a general taxa-classification model. This work has demonstrated the possibility of developing tree-classification models that can identify tree species from outside their training area and that semi-supervised deep learning may potentially utilize the untapped terabytes of unlabeled forest imagery. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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26 pages, 6739 KiB

Open AccessArticle

Pansharpening Based on Multimodal Texture Correction and Adaptive Edge Detail Fusion

by Danfeng Liu, Enyuan Wang, Liguo Wang, Jón Atli Benediktsson, Jianyu Wang and Lei Deng

Remote Sens. 2024, 16(16), 2941; https://doi.org/10.3390/rs16162941 - 11 Aug 2024

Viewed by 839

Abstract

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (

T_{C}

) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the

T_{C}

and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the

T_{C}

image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the

T_{C}

and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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21 pages, 9928 KiB

Open AccessArticle

DDSR: Degradation-Aware Diffusion Model for Spectral Reconstruction from RGB Images

by Yunlai Chen and Xiaoyan Zhang

Remote Sens. 2024, 16(15), 2692; https://doi.org/10.3390/rs16152692 - 23 Jul 2024

Viewed by 1223

Abstract

The reconstruction of hyperspectral images (HSIs) from RGB images is an attractive low-cost approach to recover hyperspectral information. However, existing approaches focus on learning an end-to-end mapping of RGB images and their corresponding HSIs with neural networks, which makes it difficult to ensure generalization due to the fact that they are trained on data with a specific degradation process. As a new paradigm of generative models, the diffusion model has shown great potential in image restoration, especially in noisy contexts. To address the unstable generalization ability of end-to-end models while exploiting the powerful ability of the diffusion model, we propose a degradation-aware diffusion model. The degradation process from HSI to RGB is modeled as a combination of multiple degradation operators, which are used to guide the inverse process of the diffusion model by utilizing a degradation-aware correction. By integrating the degradation-aware correction to the diffusion model, we obtain an efficient solver for spectral reconstruction, which is robust to different degradation patterns. Experiment results on various public datasets demonstrate that our method achieves competitive performance and shows a promising generalization ability. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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29 pages, 10649 KiB

Open AccessArticle

Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network

by Heng Zhang, Hanhu Liu, Ronghao Yang, Wei Wang, Qingqu Luo and Changda Tu

Remote Sens. 2024, 16(12), 2051; https://doi.org/10.3390/rs16122051 - 7 Jun 2024

Cited by 2 | Viewed by 1023

Abstract

Although extensive research shows that CNNs achieve good classification results in HSI classification, they still struggle to effectively extract spectral sequence information from HSIs. Additionally, the high-dimensional features of HSIs, the limited number of labeled samples, and the common sample imbalance significantly restrict classification performance improvement. To address these issues, this article proposes a double-branch multi-scale dual-attention (DBMSDA) network that fully extracts spectral and spatial information from HSIs and fuses them for classification. The designed multi-scale spectral residual self-attention (MSeRA), as a fundamental component of dense connections, can fully extract high-dimensional and intricate spectral information from HSIs, even with limited labeled samples and imbalanced distributions. Additionally, this article adopts a dataset partitioning strategy to prevent information leakage. Finally, this article introduces a hyperspectral geological lithology dataset to evaluate the accuracy and applicability of deep learning methods in geology. Experimental results on the geological lithology hyperspectral dataset and three other public datasets demonstrate that the DBMSDA method exhibits superior classification performance and robust generalization ability compared to existing methods. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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17 pages, 6317 KiB

Open AccessArticle

Spectral Reconstruction from Thermal Infrared Multispectral Image Using Convolutional Neural Network and Transformer Joint Network

by Enyu Zhao, Nianxin Qu, Yulei Wang and Caixia Gao

Remote Sens. 2024, 16(7), 1284; https://doi.org/10.3390/rs16071284 - 5 Apr 2024

Cited by 2 | Viewed by 1653

Abstract

Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth’s surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely sensed imagery necessitates more complex and higher-precision sensors, which in turn leads to higher research and operational costs. In this study, a novel Convolutional Neural Network (CNN)–Transformer combined block, termed CTBNet, is proposed to address the challenge of thermal infrared multispectral image spectral reconstruction. Specifically, the CTBNet comprises blocks that integrate CNN and Transformer technologies (CTB). Within these CTBs, an improved self-attention mechanism is introduced, which not only considers features across spatial and spectral dimensions concurrently, but also explicitly extracts incremental features from each channel. Compared to other algorithms, the proposed method more closely aligns with the true spectral curves in the reconstruction of hyperspectral images across the spectral dimension. Through a series of experiments, this approach has been proven to ensure robustness and generalizability, outperforming some state-of-the-art algorithms across various metrics. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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20 pages, 5955 KiB

Open AccessArticle

CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field

by Qiang Wu, Liang Huang, Bo-Hui Tang, Jiapei Cheng, Meiqi Wang and Zixuan Zhang

Remote Sens. 2024, 16(6), 1061; https://doi.org/10.3390/rs16061061 - 16 Mar 2024

Viewed by 1681

Abstract

Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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22 pages, 8724 KiB

Open AccessArticle

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

by Hongzhe Zhang, Shou Feng, Di Wu, Chunhui Zhao, Xi Liu, Yuan Zhou, Shengnan Wang, Hongtao Deng and Shuang Zheng

Remote Sens. 2024, 16(3), 478; https://doi.org/10.3390/rs16030478 - 26 Jan 2024

Cited by 3 | Viewed by 2612

Abstract

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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18 pages, 6951 KiB

Open AccessArticle

Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

by Yulei Wang, Haipeng Ma, Yuchao Yang, Enyu Zhao, Meiping Song and Chunyan Yu

Remote Sens. 2024, 16(2), 224; https://doi.org/10.3390/rs16020224 - 5 Jan 2024

Cited by 3 | Viewed by 1265

Abstract

As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of attention with good effect. However, many of them lack the self-supervision of representations and ignore the multi-level spectral–spatial information of HSI and the connectivity of subspaces. To this end, this paper proposes a novel self-supervised multi-level representation learning fusion-based maximum entropy subspace clustering (MLRLFMESC) method for hyperspectral band selection. Firstly, to learn multi-level spectral–spatial information, self-representation subspace clustering is embedded between the encoder layers of the deep-stacked convolutional autoencoder and its corresponding decoder layers, respectively, as multiple fully connected layers to achieve multi-level representation learning (MLRL). A new auxiliary task is constructed for multi-level representation learning and multi-level self-supervised training to improve its capability of representation. Then, a fusion model is designed to fuse the multi-level spectral–spatial information to obtain a more distinctive coefficient matrix for self-expression, where the maximum entropy regularization (MER) method is employed to promote connectivity and the uniform dense distribution of band elements in each subspace. Finally, subspace clustering is conducted to obtain the final band subset. Experiments have been conducted on three hyperspectral datasets, and the corresponding results show that the proposed MLRLFMESC algorithm significantly outperforms several other band selection methods in classification performance. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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22 pages, 15242 KiB

Open AccessArticle

Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet)

by Jing Wang, Jiaqing Miao, Gaoping Li, Ying Tan, Shicheng Yu, Xiaoguang Liu, Li Zeng and Guibing Li

Remote Sens. 2024, 16(1), 75; https://doi.org/10.3390/rs16010075 - 24 Dec 2023

Viewed by 1551

Abstract

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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27 pages, 7141 KiB

Open AccessArticle

TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based Hyperspectral Image Classification

by Ping Zhang, Haiyang Yu, Pengao Li and Ruili Wang

Remote Sens. 2023, 15(22), 5331; https://doi.org/10.3390/rs15225331 - 12 Nov 2023

Cited by 4 | Viewed by 2431

Abstract

Hyperspectral images’ (HSIs) classification research has seen significant progress with the use of convolutional neural networks (CNNs) and Transformer blocks. However, these studies primarily incorporated Transformer blocks at the end of their network architectures. Due to significant differences between the spectral and spatial features in HSIs, the extraction of both global and local spectral–spatial features remains incomplete. To address this challenge, this paper introduces a novel method called TransHSI. This method incorporates a new spectral–spatial feature extraction module that leverages 3D CNNs to fuse Transformer to extract the local and global spectral features of HSIs, then combining 2D CNNs and Transformer to capture the local and global spatial features of HSIs comprehensively. Furthermore, a fusion module is proposed, which not only integrates the learned shallow and deep features of HSIs but also applies a semantic tokenizer to transform the fused features, enhancing the discriminative power of the features. This paper conducts experiments on three public datasets: Indian Pines, Pavia University, and Data Fusion Contest 2018. The training and test sets are selected based on a disjoint sampling strategy. We perform a comparative analysis with 11 traditional and advanced HSI classification algorithms. The experimental results demonstrate that the proposed method, TransHSI algorithm, achieves the highest overall accuracies and kappa coefficients, indicating a competitive performance. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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17 pages, 6875 KiB

Open AccessArticle

Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images

by Jiaqing Zhang, Jie Lei, Weiying Xie and Daixun Li

Remote Sens. 2023, 15(17), 4255; https://doi.org/10.3390/rs15174255 - 30 Aug 2023

Viewed by 1287

Abstract

The fusion of hyperspectral and LiDAR images plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, such as Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we propose an Invariant Attribute-driven Binary Bi-branch Classification (IABC) method, which is a unified network that combines a binary Convolutional Neural Network (CNN) and a GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structural representation of HSI and LiDAR images in the spectral–spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrate the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for the accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling the real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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15 pages, 10968 KiB

Open AccessTechnical Note

RANet: Relationship Attention for Hyperspectral Anomaly Detection

by Yingzhao Shao, Yunsong Li, Li Li, Yuanle Wang, Yuchen Yang, Yueli Ding, Mingming Zhang, Yang Liu and Xiangqiang Gao

Remote Sens. 2023, 15(23), 5570; https://doi.org/10.3390/rs15235570 - 30 Nov 2023

Cited by 2 | Viewed by 1279

Abstract

Hyperspectral anomaly detection (HAD) is of great interest for unknown exploration. Existing methods only focus on local similarity, which may show limitations in detection performance. To cope with this problem, we propose a relationship attention-guided unsupervised learning with convolutional autoencoders (CAEs) for HAD, called RANet. First, instead of only focusing on the local similarity, RANet, for the first time, pays attention to topological similarity by leveraging the graph attention network (GAT) to capture deep topological relationships embedded in a customized incidence matrix from absolutely unlabeled data mixed with anomalies. Notably, the attention intensity of GAT is self-adaptively controlled by adjacency reconstruction ability, which can effectively reduce human intervention. Next, we adopt an unsupervised CAE to jointly learn with the topological relationship attention to achieve satisfactory model performance. Finally, on the basis of background reconstruction, we detect anomalies by the reconstruction error. Extensive experiments on hyperspectral images (HSIs) demonstrate that our proposed RANet outperforms existing fully unsupervised methods. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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13 pages, 15032 KiB

Open AccessTechnical Note

Retrieval of Land Surface Temperature over Mountainous Areas Using Fengyun-3D MERSI-II Data

by Yixuan Xue, Xiaolin Zhu, Zihao Wu and Si-Bo Duan

Remote Sens. 2023, 15(23), 5465; https://doi.org/10.3390/rs15235465 - 23 Nov 2023

Cited by 1 | Viewed by 1530

Abstract

Land surface temperature (LST) is an important physical quantity in the energy exchange of hydrothermal cycles between the land and near-surface atmosphere at regional and global scales. However, the traditional thermal infrared transfer equation (RTE) and LST retrieval algorithms are always based on the underlying assumptions of homogeneity and isotropy, which ignore the terrain effect influence of a heterogeneous topography. It can cause significant errors when traditional RTE and other algorithms are used to retrieve LST in such mountainous research. In this study, the mountainous thermal infrared transfer model considering terrain effect correction is used to retrieve the mountainous LST using FY-3D MERSI-II data, and the in situ site data are simultaneously utilized to evaluate the performance of the iterative single-channel algorithm. The elevation of this study region ranges from 500 m to 2200 m, whereas the minimum SVF can reach 0.75. Results show that the spatial distribution of the retrieved LST is similar to topographic features, and the LST has larger values in the lower valley and smaller values in the higher ridge. In addition, the overall bias and RMSE between the retrieved LSTs and five in situ stations are respectively −0.70 K and 2.64 K, which demonstrates this iterative single-channel algorithm performs well in taking into account the terrain effect influence. Accuracy of the LST estimation is meaningful for mountainous ecological environmental monitoring and global climate research. Such an adjacent terrain effect correction should be considered in future research on complex terrains, especially with high spatial resolution TIR data. Full article

(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)

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