Search Results - "Zhang, Liangpei"

A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images by Huanfeng Shen, Huanfeng Shen, Liangpei Zhang, Liangpei Zhang

“…Remotely sensed images often suffer from the common problems of stripe noise and random dead pixels. The techniques to recover a good image from the…”
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An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery by Huang, Xin, Zhang, Liangpei

“…In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas…”
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A Discriminative Metric Learning Based Anomaly Detection Method by Du, Bo, Zhang, Liangpei

“…Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those…”
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An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery by Huang, Xin, Zhang, Liangpei

“…In recent years, the resolution of remotely sensed imagery has become increasingly high in both the spectral and spatial domains, which simultaneously provides…”
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Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images by Du, Bo, Ru, Lixiang, Wu, Chen, Zhang, Liangpei

“…Change detection has been a hotspot in the remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images,…”
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Spectral-Spatial Unified Networks for Hyperspectral Image Classification by Xu, Yonghao, Zhang, Liangpei, Du, Bo, Zhang, Fan

“…In this paper, we propose a spectral-spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification. Different…”
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Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration by He, Wei, Zhang, Hongyan, Zhang, Liangpei, Shen, Huanfeng

“…In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix…”
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FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification by Zheng, Zhuo, Zhong, Yanfei, Ma, Ailong, Zhang, Liangpei

“…Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning-based HSI classifiers…”
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Scene Classification via a Gradient Boosting Random Convolutional Network Framework by Zhang, Fan, Du, Bo, Zhang, Liangpei

“…Due to the recent advances in satellite sensors, a large amount of high-resolution remote sensing images is now being obtained each day. How to automatically…”
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Saliency-Guided Unsupervised Feature Learning for Scene Classification by Zhang, Fan, Du, Bo, Zhang, Liangpei

“…Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to…”
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Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network by Wei, Yancong, Yuan, Qiangqiang, Shen, Huanfeng, Zhang, Liangpei

“…In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the impressive effectiveness of deep neural networks has recently been…”
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Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network by Yuan, Qiangqiang, Zhang, Qiang, Li, Jie, Shen, Huanfeng, Zhang, Liangpei

“…Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In…”
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A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection by Zhang, Yuxiang, Du, Bo, Zhang, Liangpei, Wang, Shugen

“…Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract…”
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An Integrated Framework for the Spatio-Temporal-Spectral Fusion of Remote Sensing Images by Shen, Huanfeng, Meng, Xiangchao, Zhang, Liangpei

“…Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework…”
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Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing by Wang, Xinyu, Zhong, Yanfei, Zhang, Liangpei, Xu, Yanyan

“…In recent years, blind source separation (BSS) has received much attention in the hyperspectral unmixing field due to the fact that it allows the simultaneous…”
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Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network by Chen, Hongruixuan, Wu, Chen, Du, Bo, Zhang, Liangpei, Wang, Le

“…With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly…”
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A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion by Wu, Chen, Du, Bo, Cui, Xiaohui, Zhang, Liangpei

“…Post-classification with multi-temporal remote sensing images is one of the most popular change detection methods, providing the detailed “from-to” change…”
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Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing by He, Wei, Zhang, Hongyan, Zhang, Liangpei

“…Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their corresponding fractional abundances, is an important task for…”
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Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection by Zhang, Fan, Du, Bo, Zhang, Liangpei, Xu, Miaozhong

“…Aircraft detection from very high resolution (VHR) remote sensing images has been drawing increasing interest in recent years due to the successful civil and…”
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Random-Selection-Based Anomaly Detector for Hyperspectral Imagery by Du, Bo, Zhang, Liangpei

“…Anomaly detection in hyperspectral images is of great interest in the target detection domain since it requires no prior information and makes full use of the…”
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