Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference

Autor:	Dao, Minh, Xiang, Xiang, Ayhan, Bulent, Kwan, Chiman, Tran, Trac D.
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data. The proposed model contains two-processing steps in which the first step separate and then suppress the cloud information presenting in the data set using an RPCA algorithm and the second step detect the burnscar area in the low-rank component output of the first step. Experiments are conducted on the public MODIS dataset available at NASA official website. Comment: It is not a publishable version at this point as there is no IP coverage at the moment
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1605.00287 Zobrazit plný text záznamu View this record from Arxiv