218 citations · 974 across the 14 of their papers we have counts for
14 papers
Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection
Haopeng Zhang, Zijing Lin, Feng Gao +3
Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attentions in the remote sensing community. However, existing SAR change detect…
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Nanqing Liu, Xun Xu, Turgay Celik +2
Object detection in remote sensing images relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustiv…
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification
Meng Wang, Feng Gao, Junyu Dong +2
The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown rem…
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
Desen Meng, Feng Gao, Junyu Dong +2
Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existi…
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
Xin-Ru Feng, Heng-Chao Li, Rui Wang +3
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix fa…
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification
Jin-Yu Yang, Heng-Chao Li, Wen-Shuai Hu +2
Recently, graph convolutional networks (GCNs) have been developed to explore spatial relationship between pixels, achieving better classification performance of hyperspectral image…
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification
Heng-Chao Li, Wen-Shuai Hu, Wei Li +3
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this paper, we propose a new…
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network
Junjie Wang, Feng Gao, Junyu Dong +2
Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past fe…
SAR Image Change Detection Based on Multiscale Capsule Network
Yunhao Gao, Feng Gao, Junyu Dong +1
Traditional synthetic aperture radar image change detection methods based on convolutional neural networks (CNNs) face the challenges of speckle noise and deformation sensitivity.…
Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network
Yunhao Gao, Feng Gao, Junyu Dong +2
Synthetic aperture radar (SAR) image change detection is a critical yet challenging task in the field of remote sensing image analysis. The task is non-trivial due to the following…
SAR Image Change Detection Based on Multiscale Capsule Network
Yunhao Gao, Feng Gao, Junyu Dong +1
Traditional change detection methods based on convolutional neural networks (CNNs) face the challenges of speckle noise and deformation sensitivity for synthetic aperture radar ima…
Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network
Xiaofan Qu, Feng Gao, Junyu Dong +2
Change detection from synthetic aperture radar (SAR) imagery is a critical yet challenging task. Existing methods mainly focus on feature extraction in spatial domain, and little a…
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification
Wen-Shuai Hu, Heng-Chao Li, Lei Pan +3
In recent years, deep learning has presented a great advance in hyperspectral image (HSI) classification. Particularly, long short-term memory (LSTM), as a special deep learning st…
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
Fan Zhang, Chen Hu, Qiang Yin +3
The outstanding pattern recognition performance of deep learning brings new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). However, there is a l…