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Shao-Shan Chiang
Researcher at University of Maryland, Baltimore County
Publications - 10
Citations - 947
Shao-Shan Chiang is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Contextual image classification & Image processing. The author has an hindex of 8, co-authored 10 publications receiving 885 citations. Previous affiliations of Shao-Shan Chiang include Lunghwa University of Science and Technology & University of Baltimore.
Papers
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Journal ArticleDOI
Anomaly detection and classification for hyperspectral imagery
Chein-I Chang,Shao-Shan Chiang +1 more
TL;DR: Experiments show that anomaly classification performs very differently from anomaly detection, which can be implemented in a three-stage process, first by anomaly detection to find potential targets, followed by target discrimination to cluster the detected anomalies into separate target classes, and concluded by a classifier to achieve target classification.
Journal ArticleDOI
Unsupervised target detection in hyperspectral images using projection pursuit
TL;DR: The proposed PP method is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest while utilizing a projection index to explore projections of interestingness.
Journal ArticleDOI
Real-time processing algorithms for target detection and classification in hyperspectral imagery
TL;DR: The authors present a linearly constrained minimum variance (TCMV) beamforming approach to real time processing algorithms for target detection and classification in hyperspectral imagery and further generalized the LCMV approach so that the targets can be detected and classified simultaneously.
Journal ArticleDOI
Linear spectral random mixture analysis for hyperspectral imagery
TL;DR: This paper considers an application of ICA to the LSMA, referred to as ICA-based linear spectral random mixture analysis (LSRMA), which describes an image pixel as a random source resulting from a random composition of multiple spectral signatures of distinct materials in the image.
Proceedings ArticleDOI
Unsupervised hyperspectral image analysis using independent component analysis
TL;DR: In this paper, an ICA-based approach is proposed for hyperspectral image analysis, which can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources.