Adaptive branch and bound algorithm (ABB) for use on hyperspectral data

Songyot Nakariyakul; David P. Casasent

doi:10.1117/12.666175

4 May 2006 Adaptive branch and bound algorithm (ABB) for use on hyperspectral data

Songyot Nakariyakul, David P. Casasent

Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII; 62332E (2006) https://doi.org/10.1117/12.666175
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

We propose a new adaptive branch and bound (ABB) algorithm for selecting the optimal subset of features in hyperspectral applications. The algorithm improves the search speed by avoiding unnecessary criterion function calculations at nodes in the solution tree. Our algorithm includes the following new properties: (i) ordering the tree nodes by the significance of features during construction of the tree, (ii) obtaining a large "good" initial bound by a floating search method, (iii) a new method to select an initial starting search level in the tree, and (iv) a new adaptive jump search strategy to select subsequent search levels to avoid redundant criterion function calculations. Our experimental results for two databases demonstrate that our method is significantly faster than other versions of the branch and bound algorithm.

Citation Download Citation

Songyot Nakariyakul and David P. Casasent "Adaptive branch and bound algorithm (ABB) for use on hyperspectral data", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62332E (4 May 2006); https://doi.org/10.1117/12.666175

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