Titlebook: Real-Time Progressive Hyperspectral Image Processing; Endmember Finding an Chein-I Chang Book 2016 Springer Science+Business Media, LLC 201

AGATE

Geometric-Unconstrained Sequential Endmember Finding: Orthogonal Projection Analysisbundance constraints. Pixel Purity Index (PPI) is the earliest algorithm developed by Boardman (International Geoscience Remote Sensing Symposium, 4:2369–2371, .) taking advantage of OP to find endmembers in hyperspectral images. It has become very popular and has enjoyed publicity because of its av

Epidural-Space

Fully Abundance-Constrained Sequential Endmember Finding: Linear Spectral Mixture Analysised for data unmixing. It assumes that data sample vectors can be represented by a set of signatures via a Linear Mixing Model (LMM) by which data sample vectors can then be unmixed by FCLS in terms of the abundance fractions of these signatures present in the data sample vectors subject to two physi

linguistics

Fully Geometric-Constrained Progressive Endmember Finding: Growing Simplex Volume Analysishap. . and shown to be a promising approach to finding endmembers. As a matter of fact, the Simplex Growing Algorithm (SGA) developed by Chang et al. (.) for GSVA does the same as the N-finder algorithm (N-FINDR) developed by Winter (., .) for SVA. The key difference between these two is how endmemb

Occipital-Lobe

Partially Geometric-Constrained Progressive Endmember Finding: Growing Convex Cone Volume Analysises for a given fixed number of convex cone vertices in the same way that N-FINDR maximizes simplex volumes in Chap. . for a given fixed number of simplex vertices. Its main idea is to project a convex cone onto a hyperplane so that the projected convex cone becomes a simplex. With this advantage, wh

delegate

Geometric-Unconstrained Progressive Endmember Finding: Orthogonal Projection Analysisr is fully processed sample-by-sample one after another for a given fixed set of skewers to produce its own final PPI count. This chapter presents a rather different version of IPPI, referred to as Progressive IPPI (P-IPPI) by interchanging two iterative loops carried out in C-IPPI. In other words,

Generosity

Affiliation

定点

Anomaly Discrimination and Categorization categorize anomalies. Despite that anomaly discrimination was also studied by Adaptive Causal Anomaly Detector (ACAD) in Chap. . anomaly discrimination presented in this chapter is quite different from ACAD in the sense that it does not require causality as well as building an anomaly library as AC