Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery
Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (H…
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly p…
Change DetectionSparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Hyperspectral unmixing aims at estimating material signatures (known as endmembers) and the corresponding proportions (referred to abundances), which is a critical preprocessing step in various hyperspectral imagery appl…
Automatic Target Detection for Sparse Hyperspectral Images
In this work, a novel target detector for hyperspectral imagery is developed. The detector is independent on the unknown covariance matrix, behaves well in large dimensions, distributional free, invariant to atmospheric …
Dictionary learning for clustering on hyperspectral images
Dictionary learning and sparse coding have been widely studied as mechanisms for unsupervised feature learning. Unsupervised learning could bring enormous benefit to the processing of hyperspectral images and to other re…
ClusteringDictionary Learning