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Shared-Private Decoupling-Based Multilevel Feature Alignment Semisupervised Learning for HSI and LiDAR Classification
The joint classification methods of hyperspectral image (HSI) and light detection and ranging (LiDAR) data based on deep learning have demonstrated exceptional classification performance with sufficient labeled samples. However, it is expensive and time-consuming to acquire labeled data. To address this limitation, we propose a shared-private decoupling-based multilevel feature alignment semisuper...Show More
Graph Representation Learning-Guided Diffusion Model for Hyperspectral Change Detection
Xinyu Ding;Jiahui Qu;Wenqian Dong;Tongzhen Zhang;Nan Li;Yufei Yang
Due to its capability to monitor subtle changes occurring on the Earth’s surface, hyperspectral images change detection (HSI-CD) has emerged as a focal research area in the field of remote sensing. Recently, diffusion models have demonstrated remarkable performance in the field of HSI-CD. However, vanilla diffusion models are mostly constructed by CNN, which struggles to model global context relat...Show More
Abundance Matrix Correlation Analysis Network Based on Hierarchical Multihead Self-Cross-Hybrid Attention for Hyperspectral Change Detection
Wenqian Dong;Jingyu Zhao;Jiahui Qu;Song Xiao;Nan Li;Shaoxiong Hou;Yunsong Li
Hyperspectral image (HSI) change detection is a technique for detecting the changes between the multitemporal HSIs of the same scene. Many existing change detection methods have achieved good results, but there still exist problems as follows: 1) mixed pixels exist in HSI due to the low spatial resolution of hyperspectral sensor and other external interference and 2) many existing deep learning-ba...Show More
Low-Rank Nonlocal Representation for Remote Sensing Scene Classification
The nonlocal mechanism (NM) has shown its effectiveness in many real-world applications. However, it is usually criticized for its costly computation complexity in time and space, which are both $\mathscr {O}(H^{2}W^{2})$ , where $H\times W$ is the spatial dimension of input feature maps in height and width. In this letter, we first show that the NM can be expressed as a low-rank representation...Show More
Positional Context Aggregation Network for Remote Sensing Scene Classification
To capture the long-range dependence of an input image for remote sensing scene (RSS) classification, in this letter, we propose a general positional context aggregation (PCA) module in deep convolutional neural networks. The PCA module is with the form of self-attention mechanism, in which two proposed blocks, the spatial context aggregation (SCA) and the relative position encoding (RPE), are use...Show More
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