Recent Advances in Multispectral and Hyperspectral Image Analysis and Classification

Dear Colleagues,

Multispectral/hyperspectral remote sensing imaging technology is able to analyze the structure and material characteristics of surface features by simultaneously obtaining spatial and spectral information, and it also has the ability to distinguish more types of features and invert an increased number of features. Currently, multispectral/hyperspectral remote sensing is widely used in many fields such as precision agriculture, geological surveys, land and resource surveys, atmospheric environment analysis, and military target detection.

Obtaining land/target distribution and related characteristic information from multi hyperspectral remote sensing images requires the use of many advanced analytical techniques, such as hyperspectral classification, object detection and recognition, cross-modal analysis, intrinsic image decomposition, multi hyperspectral image fusion, multi hyperspectral intrinsic decomposition, high-order signal processing, etc. Therefore, the main purpose of this Special Issue is to solicit and showcase recent techniques used for analyzing and classifying multispectral/hyperspectral remote sensing images, and to promote the development of spectral imaging technologies, especially in the fields of relative reflectance extraction, universal classification, robust target detection, etc.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Multispectral/hyperspectral imaging systems;
• Multispectral/hyperspectral classification and target detection method;
• Multispectral and hyperspectral fusion and super-resolution method;
• Multitemporal remote sensing change detection method;
• Remote sensing signal representation theory and intrinsic image decomposition;
• Multispectral/hyperspectral scene classification technology;
• Cross-domain (including temporal, scene, regional) analysis method.

Dr. Guoming Gao
Dr. Mercedes E. Paoletti
Guest Editors

Manuscript Submission Information

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• hyperspectral classification
• intrinsic image decomposition
• scene classification
• high-order tensor analysis
• cross-domain adaptation
• hyperspectral and multispectral image fusion
• deep learning

Title: Joint Classification of Hyperspectral and LiDAR Data via Mul-ti-probability Decision Fusion Method
Authors: Tao Chen; Sizuo Chen; Luying Chen; Huayue Chen; Bochuan Zheng; Wu Deng
Affiliation: China West Normal University
Abstract: With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, mul-ti-source remote sensing image data can provide complementary information for recognizing the same feature. The effective utilization of remote sensing image data from various sources can en-hance the extraction of image features and improve the accuracy of feature recognition. Hyper-spectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives, and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, high time complexity, low classification accuracy, and unstable classification results. In order to address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multi-probability decision fusion method (PRDRMF) for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data were downscaled using the principal component-relative total variation (PRTV) method to remove redundant information, respectively. In the multi-feature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multi-attribute profile (EMAP) for the two types of data after dimensionality reduction. Then, the four extracted features are inputted into the corresponding kernel-extreme learning machine (KELM) with a simple structure and good classi-fication performance to obtain four classification probability matrices (CPMs). Finally, the four CPMs were fused using a multi-probability decision fusion method to obtain the optimal classi-fication results. Comparison experiments on three classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method.

Title: Enhancing Binary Change Detection in Hyperspectral Images Using an Efficient Dimensionality Reduction Technique within Adversarial Learning
Authors: Amel Oubara; Falin Wu; Guoxin Qu; Reza Maleki; Gongliu Yang
Affiliation: SNARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Abstract: Detecting binary changes in co-registered bitemporal hyperspectral images (HSI) using deep learning methods is challenging due to the high dimensionality of spectral data and significant variations between images. To address this challenge, previous approaches often use dimensionality reduction methods separately from the change detection network, leading to less accurate results. In this paper, we propose an end-to-end fully connected adversarial network (EFC-AdvNet) for binary change detection, which efficiently reduces the dimensionality of bi-temporal HSIs and simultaniously detects changes between them. This is accomplished by extracting critical spectral features at the pixel level through a Self-Spectral Reconstruction (SSR) module working in conjunction with an Adversarial Change Detection (Adv-CD) module to effectively delineate changes between bitemporal HSIs. The SSR module employs a fully connected autoencoder for hyperspectral dimensionality reduction and spectral feature extraction. By integrating the encoder segment of the SSR module with the change detection network of the Adv-CD module, we create a generator that directly produces highly accurate change maps. This joint learning approach enhances both feature extraction and change detection capabilities. The proposed network is trained using a comprehensive loss function derived from the concurrent learning of the SSR and Adv-CD modules, establishing EFC-AdvNet as a robust end-to-end network for hyperspectral binary change detection. Experimental evaluations of EFC-AdvNet on three public hyperspectral datasets demonstrate that joint learning between the SSR and Adv-CD modules improves the Overall Accuracy (OA) by 5.44%, 10.43%, and 7.52% for the Farmland, Hermiston, and River datasets, respectively, compared to separate learning approach.