Titlebook: Real-Time Recursive Hyperspectral Sample and Band Processing; Algorithm Architectu Chein-I Chang Book 2017 Springer International Publishin

Musket

吃掉

Recursive Hyperspectral Sample Processing of Maximum Likelihood Estimationxing model (ALMM) that can adapt to the signatures, referred to as virtual signatures (VSs), generated directly from data in an unsupervised and recursive manner. This chapter considers an alternative approach to RHSP-LSMA, called recursive hyperspectral sample processing of maximal likelihood estim

Apraxia

Nuance

Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Algorithmonsquare matrix, which involves excessive computing time in calculating the matrix determinant. This type of SV calculation is referred to as a determinant-based SV (DSV) calculation (Chap. .). Therefore, several preprocessing steps are suggested for DSV calculation in Chap. . to ease computational

ACRID

多节

Recursive Hyperspectral Band Processing for Passive Target Detection: Anomaly Detectiond anomaly detection, New York, 2016), where the main focus of AD is on the design and development of AD algorithms for causal processing, which is a prerequisite for real-time processing. Chapter . in this book makes use of causality to further develop various real-time processing versions of AD so

ovation

Aerate

使出神

Embolic-Stroke

Introduction,003)], removal of highly correlated interband information by data compression or data reduction, data communication, and transmission once hyperspectral imaging sensors are deployed in space. One effective means of dealing with these issues is to develop real-time hyperspectral imaging algorithms th