Improved Fukunaga-Koontz Transform with Compositional Kernel Combination for Hyperspectral Target Detection

作     者：Binol, Hamidullah 

作者机构：Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA 

出 版 物：《JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING》 (印度遥感学会杂志)

年 卷 期：2018年第46卷第10期

页      面：1605-1615页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

主　　题：Compositional kernel combination Differential evolution algorithm Fukunaga-Koontz transform Hyperspectral imagery Target detection 

摘      要：This article presents a novel supervised target detection approach on hyperspectral images based on Fukunaga-Koontz Transform (FKT) with compositional kernel combination. The Fukunaga-Koontz Transform is one of the most effective techniques for solving problems that involve two-pattern characteristics. To capture nonlinear properties of data, researchers have extended FKT to kernel FKT (KFKT) by means of kernel machines. However, the performance of KFKT depends on choosing convenient kernel functions and/or selection of the proper parameter(s). In this work, instead of selecting a single kernel for nonlinear version of FKT, we have applied a compositional kernel combination approach to capture the underlying local distributions of hyperspectral remote sensing data. Optimal parameter selection for each kernel function is achieved applying an evolutionary technique called differential evolution algorithm. The proposed new nonlinear target detection algorithm is tested for hyperspectral images. The experimental results verify that the proposed target detection algorithm has effective and promising performance compared to the conventional version for supervised target detection applications.