Results 11 to 20 of about 8,883 (219)

SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022 [PDF]

Earth System Science Data, 2022
The drawbacks of low-coverage rate in global land inevitably exist in satellite-based daily soil moisture products because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models.
Q. Zhang, Q. Yuan, T. Jin, M. Song, F. Sun   +4 more
doaj   +1 more source

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Target extraction can provide a prior knowledge for spectral unmixing, unsupervised hyperspectral image classification, and unsupervised target detection tasks, which is of great practice.
Xiaodi Shang, Tingting Yang, Sichao Han, Meiping Song, Bai Xue   +4 more
doaj   +1 more source

Robust linear unmixing with enhanced constraint of classification for hyperspectral remote sensing imagery

IET Image Processing, 2022
Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors.
Haoyang Yu, Jinxue Chi, Xiaodi Shang, Xueji Shen, Jocelyn Chanussot, Yimin Shi   +5 more
doaj   +1 more source

A HYPERSPECTRAL REMOTE SENSING FUSION TECHNOLOGY BASED ON SPECTRAL NORMALIZATION OF GF AND ZY SERIES SATELLITES [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022
Globalized surface coverage, environmental monitoring and other earth system science and high-quality global surface coverage monitoring applications urgently need basic hyperspectral remote sensing reflectance data to support.
S. Liu, H. Li, X. Wang, C. Liu, Y. Wang, P. Huang   +5 more
doaj   +1 more source

A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of a hyperspectral cube, feature extraction with 3-D convolution operation is a ...
Chunyan Yu, Rui Han, Meiping Song, Caiyu Liu, Chein-I Chang   +4 more
doaj   +1 more source

Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”

Remote Sensing, 2022
Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data.
Chein-I Chang, Meiping Song, Chunyan Yu, Yulei Wang, Haoyang Yu, Jiaojiao Li, Lin Wang, Hsiao-Chi Li, Xiaorun Li   +8 more
doaj   +1 more source

Extended Subspace Projection Upon Sample Augmentation Based on Global Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a ...
Jiaochan Hu, Xueji Shen, Haoyang Yu, Xiaodi Shang, Qiandong Guo, Bing Zhang   +5 more
doaj   +1 more source

Utilizing Hyperspectral Remote Sensing for Soil Gradation [PDF]

Remote Sensing, 2020
Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation.
Jordan Ewing, Thomas Oommen, Paramsothy Jayakumar, Russell Alger   +3 more
openaire   +2 more sources

Gabor Filter-Based Multi-Scale Dense Network Hyperspectral Remote Sensing Image Classification Technique

IEEE Access, 2023
Since hyperspectral remote sensing images are three-dimensional data cubes with spatial and spectral information, with many wavebands and high inter-band correlation, the number of training samples required for classification is greatly increased.
Chaozhu Zhang, Shengrong Zhu, Dan Xue, Song Sun   +3 more
doaj   +1 more source

Union of Class-Dependent Collaborative Representation Based on Maximum Margin Projection for Hyperspectral Imagery Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
This article proposed a novel spectral-spatial classification framework for hyperspectral image (HSI) through combining collaborative representation (CR) and maximum margin projection (MMP).
Haoyang Yu, Xiaodi Shang, Meiping Song, Jiaochan Hu, Tong Jiao, Qiandong Guo, Bing Zhang   +6 more
doaj   +1 more source