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

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Simplex Volume Calculationdata dimensionality reduction to make the matrix of full rank. The drawback of this method is that the original volume has been shrunk and the found volume of a dimensionality-reduced simplex is not the true original SV. The other is to use singular value decomposition to find singular values for ca

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Target-Specified Virtual Dimensionality for Hyperspectral Imageryrent conclusions have been drawn about VD. This issue was realized by Chang (Hyperspectral data processing: algorithm design and analysis, Wiley, Hoboken, 2013), where VD was defined by two types of criteria, data characterization-driven criteria and data representation-driven criteria. This chapter

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Real-Time Recursive Hyperspectral Sample Processing for Active Target Detection: Constrained Energy s yet to be visited should be involved in data processing. Such a property is generally called ., which has unfortunately received little attention in real-time hyperspectral data processing in recent years. This chapter investigates one of the well-known active hyperspectral target detection techni

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Real-Time Recursive Hyperspectral Sample Processing for Passive Target Detection: Anomaly Detectiondetection is a major task in hyperspectral image analysis and has been studied extensively in the literature. Applications of passive target detection include surveillance and monitoring, where no knowledge is required .. Of particular interest in passive target detection is AD, which is generally p

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Recursive Hyperspectral Sample Processing of Automatic Target Generation Process, which are augmented by newly found targets one at a time. This process requires a significant amount of computing time, which will grow exponentially as the number of targets is increased. Another is that it does not have an automatic stopping rule to terminate the process in real time. This chapt

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Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Algorithmuntered in finding SVs using a matrix determinant calculation, in addition to another issue: the calculated SV may not be a true SV, as pointed out in Chap. .. To resolve this dilemma, Chap. . developed an orthogonal projection (OP)-based growing simplex volume analysis (GSVA) approach, called ortho