Results 151 to 160 of about 12,268,087 (190)

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification-A Review. [PDF]

J Imaging
Papadopoulos S, Koukiou G, Anastassopoulos V.   +2 more
europepmc   +1 more source

Remote sensing based forest cover classification using machine learning. [PDF]

Sci Rep
Aziz G, Minallah N, Saeed A, Frnda J, Khan W.   +4 more
europepmc   +1 more source

Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data. [PDF]

Front Plant Sci
Zhou W, Shu Q, Xia C, Xu L, Xiang Q, Fu L, Yang Z, Wang S.   +7 more
europepmc   +1 more source

Quantitative Retrieval of Soil Salinity in Arid Regions: A Radar Feature Space Approach with Fully Polarimetric SAR Data. [PDF]

Sensors (Basel)
Nurmemet I, Aihaiti A, Aili Y, Lv X, Li S, Qin Y.   +5 more
europepmc   +1 more source

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Variational Learning of Mixture Wishart Model for PolSAR Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2019
The phase difference, amplitude product, and amplitude ratio between two polarizations are important discriminators for terrain classification, which derives a significant statistical-distribution-based polarimetric synthetic aperture radar (PolSAR) image classification.
Qian Wu, Biao Hou, Zaidao Wen
exaly   +3 more sources

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

Remote Sensing, 2021
In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification.
Yachao Zhang, Yanyun Qu
exaly   +2 more sources

Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network

Remote Sensing, 2019
Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the ...
Yan Wu, , Peng Zhang
exaly   +2 more sources

Riemannian sparse coding for classification of PolSAR images

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Hermitian positive definite (HPD) covariance matrices form one of the most widely-used data representations in PolSAR applications. However, most of these applications either use statistical distribution models on the PolSAR covariance matrices or polarimetric target decomposition. In this paper, we study HPD matrices for PolSAR image classification in
Wen Yang 0001, Neng Zhong, Xiangli Yang, Anoop Cherian   +3 more
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Exploring Convolutional Lstm for Polsar Image Classification

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important applications in Pol-SAR image processing. More and more deep learning methods are applied to PolSAR image classification. As we know, the polarimetric response of a target is related to the orientation of the target, but the features in rotation domain are ...
Lei Wang 0068, Xin Xu 0005, Hao Dong 0006, Rong Gui, Rui Yang 0012, Fangling Pu   +5 more
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Classification-oriented hyperspectral and PolSAR images synergic processing

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
Classification is one of the most important applications in the field of remote sensing. How to improve the accuracy of classification is the critical topic that has long obsessed the researchers. In this paper, a fusion method based on a synergic use of hyperspectral data and Polarimetric SAR (PolSAR) data is presented.
Tong Li 0010, Junping Zhang, Honglei Zhao, Cuiping Shi   +3 more
openaire   +2 more sources