Unsupervised spectral-spatial feature selection-based camouflaged object detection using VNIR hyperspectral camera.

Sungho Kim

doi:10.1155/2015/834635

Abstract

The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications. Conventional supervised learning methods for hyperspectral images can be a feasible solution. Such approaches, however, require a priori information of a camouflaged object and background. This letter proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. The statistical distance metric can generate candidate feature bands and further analysis of the entropy-based spatial grouping property can trim the useless feature bands. Camouflaged objects can be detected better with less computational complexity by optical spectral-spatial feature analysis.

Highlights

The development of an image sensor and optical dispersion technology has made it possible to capture hyperspectral image data with lower prices, such as SPECIM or Honeywell products [1]
From a test input hypercube, the proposed system generates candidate bands based on statistical distance analysis
Experimental comparisons with the baseline methods validated the outperformance of the proposed method in terms of the detection rate and false alarm rate with a minimal number of bands for a real test set

Summary

Introduction

The development of an image sensor and optical dispersion technology has made it possible to capture hyperspectral image data with lower prices, such as SPECIM or Honeywell products [1]. Band selection can be achieved by supervised or unsupervised learning The former requires a set of labeled training databases and produces the high accuracy of detection performance [4,5,6,7]. This study adopted the unsupervised learning-based band selection scheme for its convenience in automatic camouflaged or abnormal region detection. Several studies proposed a range of band selection or elimination methods in unsupervised learning approaches focusing only on spectral analysis. In the first stage of spectral feature analysis, a new statistical distance measure in the ranking-based method instead of the band clustering method was proposed due to the high computational complexity.

Proposed Camouflaged Object Detection Method

Spectral-Spatial Analysis-Based Band Selection

Experimental Results

Conclusions

Conflict of Interests