This paper presents a novel camouflaged target detection method using spectral and spatial feature fusion. Conventional unsupervised learning methods using spectral information only can be feasible solutions. Such approaches, however, sometimes produce incorrect detection results because spatial information is not considered. This paper proposes a novel band feature selection method by considering both the spectral distance and spatial statistics after spectral normalization for illumination invariance. The statistical distance metric can generate candidate feature bands and further analysis of the spatial grouping property can trim the useless feature bands. Camouflaged targets can be detected better with less computational complexity by the spectral-spatial feature fusion.
This paper presents a concealed target detection based on the intersection kernel Support Vector Machine (SVM).
Hyperspectral imagers are widely used in the field of target detection and material analysis. In military applications, it
can be used to border protection, concealed target detection, reconnaissance and surveillance. If disguised enemies not
detected in advance, the damage of allies will be catastrophic by unexpected attack. Concealed object detection using
radar and terahertz method is widely used. However, these active techniques are easily exposed to the enemy. Electronic
Optical Counter Counter Measures (EOCCM) using hyperspectral imagers can be a feasible solution. We use the band
selected feature directly and the intersection kernel based SVM. Different materials show different spectrums although
they look similar in CCD camera. We propose novel concealed target detection method that consist of 4 step, Feature
band selection, Feature Extraction, SVM learning and target detection.