Fusion of iECO image descriptors for buried explosive hazard detection in forward-looking infrared imagery

Document Type

Conference Proceeding

Publication Date



Department of Electrical and Computer Engineering, Center for Data Sciences


Data fusion is a powerful theory that often leads to significant performance gain and/or improved robustness of a given solution. In this article, we explore how fusion can be used to advance our previously established improved Evolutionary COnstructed (iECO) image descriptor framework. The goal of iECO is to learn a diverse set of individuals (variable length chromosome in a genetic algorithm). Each iECO individual encodes a unique composition of different low-level image transformations in the context of a high-level image descriptor. Herein, we investigate multiple kernel (MK) aggregation and MK learning (MKL) for “feature-level” fusion of iECO chromosomes. Specifically, we explore MKL group lasso (MKLGL) and we put forth a new way to directly assign kernel weights from a measure defined on the kernel matrices. The proposed work is presented in the context of buried explosive hazard detection (EHD) in forward looking (FL) imagery. Experiments are reported using receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. We demonstrate that MK support vector machine (MKSVM) classification outperform single kernel SVM (SKSVM) classification and our weight assignment procedure generalizes well and outperforms MKLGL for EHD in FLIR.

Publication Title

Proceedings of SPIE 9454 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets