Results 311 to 320 of about 153,077 (360)

Hyperspectral Image Super-Resolution Meets Deep Learning: A Survey and Perspective

IEEE/CAA Journal of Automatica Sinica, 2023
Hyperspectral image super-resolution, which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation, aims to improve the spatial resolution of the hyperspectral image, which is beneficial for subsequent ...
Xinya Wang, Qian Hu, Yingsong Cheng, Jiayi Ma   +3 more
semanticscholar   +1 more source

WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF

, 2020
Unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution (which we refer to here as H2 imagery).
Yanfei Zhong, Xin Hu, Chang Luo, Xinyu Wang, Ji Zhao, L. Zhang   +5 more
semanticscholar   +1 more source

Hyperspectral Anomaly Detection With Robust Graph Autoencoders

IEEE Transactions on Geoscience and Remote Sensing, 2022
Anomaly detection of hyperspectral data has been gaining particular attention for its ability in detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants can not only extract intrinsic features automatically but also ...
Ganghui Fan, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, Jiayi Ma   +5 more
semanticscholar   +1 more source

Watermarking of hyperspectral data

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2004
Watermarking is drawing the interest of researchers in many areas due to developments in sharing resources. Watermarked data helps protect ownership rights and provides a means of detecting illegal use. In this paper, an adaptive watermarking method based on the Redundant Discrete Wavelet Transform (RDWT) is proposed and applied to hyperspectral ...
Hrishikesh Tamhankar, Lori M. Bruce, Nicolas H. Younan   +2 more
openaire   +1 more source

WaveFormer: Spectral–Spatial Wavelet Transformer for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters
Transformers have proven effective for hyperspectral image classification (HSIC) but often incorporate average pooling that results in information loss. This letter presents WaveFormer, a novel transformer-based approach that leverages wavelet transforms
Muhammad Ahmad, Usman Ghous, Muhammad Usama, Manuel Mazzara   +3 more
semanticscholar   +1 more source

Deep Hierarchical Vision Transformer for Hyperspectral and LiDAR Data Classification

IEEE Transactions on Image Processing, 2022
In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture for hyperspectral and light detection and ranging (LiDAR) data joint classification.
Zhixiang Xue, Xiong Tan, Xuchu Yu, Bing Liu, Anzhu Yu, Pengqiang Zhang   +5 more
semanticscholar   +1 more source

Snapshot hyperspectral imaging

Integrated Computational Imaging Systems, 2001
Hyperspectral imaging associates a densely sampled spectral signature with each pixel in an imager’s field of view. The collected image data are in the form of a three-dimensional data set, i.e., an image cube. The image cube is also referred to as a hypercube or an object cube.
M.R. Descour, C.E. Volin, B.K. Ford, E.L. Dereniak, P.D. Maker, D.W. Wilson   +5 more
openaire   +1 more source

Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging

, 2020
Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two
Bo Li, Xiang-ming Xu, Li Zhang, Jiwan Han, Chunsong Bian, Guangcun Li, Jiangang Liu, Liping Jin   +7 more
semanticscholar   +1 more source

Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection

IEEE Transactions on Cybernetics, 2022
Recently, low-rank representation (LRR) methods have been widely applied for hyperspectral anomaly detection, due to their potentials in separating the backgrounds and anomalies.
Minghua Wang, Qiang Wang, D. Hong, S. K. Roy, J. Chanussot   +4 more
semanticscholar   +1 more source

Blind hyperspectral denoising

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2015
In this work we propose a new formulation for hyperspectral denoising based on the Blind Compressed Sensing (BCS) framework. BCS learns the sparsifying basis during signal recovery combining the advantages of standard sparse recovery with dictionary learning. We show that our proposed formulation yields better results than a state-of-the- art technique
Hemant Kumar Aggarwal, Angshul Majumdar
openaire   +1 more source