Camouflaged Target Detection Based on Snapshot Multispectral Imaging

Ying Shen,Feng Huang,Wenfu Lin,Liqiong Chen,Shu Wang,Jie Li

doi:10.3390/rs13193949

Abstract

The spectral information contained in the hyperspectral images (HSI) distinguishes the intrinsic properties of a target from the background, which is widely used in remote sensing. However, the low imaging speed and high data redundancy caused by the high spectral resolution of imaging spectrometers limit their application in scenarios with the real-time requirement. In this work, we achieve the precise detection of camouflaged targets based on snapshot multispectral imaging technology and band selection methods in urban-related scenes. Specifically, the camouflaged target detection algorithm combines the constrained energy minimization (CEM) algorithm and the improved maximum between-class variance (OTSU) algorithm (t-OTSU), which is proposed to obtain the initial target detection results and adaptively segment the target region. Moreover, an object region extraction (ORE) algorithm is proposed to obtain a complete target contour that improves the target detection capability of multispectral images (MSI). The experimental results show that the proposed algorithm has the ability to detect different camouflaged targets by using only four bands. The detection accuracy is above 99%, and the false alarm rate is below 0.2%. The research achieves the effective detection of camouflaged targets and has the potential to provide a new means for real-time multispectral sensing in complex scenes.

Highlights

Hyperspectral target detection is widely used in industry, agriculture, and urban remote sensing [1]
The optimal clustering framework (OCF) [14] constructs an optimal cluster structure on Hyperspectral images (HSI), and the discriminative information between the bands is evaluated based on a cluster ranking strategy for the achieved structure to determine the optimal band combination
Compared the image scene, wherebetween the ASRC-net is circled by the solid line and the AIRC-net is circled gravated the mismatch the prior spectrum and the in-scene signature, causing acquired by a 16 mm focal length lens shown in Figure 15a, the target occupies a mucha by the dashed line; (b) The false-color image of the BT scene; (c) The reference image of the ASRC-net; (d) The reference degradation in theinperformance of the algorithms this experimental scenario, we larger proportion the image acquired by a 35 mm[5,7]

Summary

Introduction

Hyperspectral target detection is widely used in industry, agriculture, and urban remote sensing [1]. Hyperspectral images (HSI) are often presented as spectral data cubes measured for each pixel as a spectral vector. Shaw, and other researchers [4,5,6,7] in MIT’s Lincoln laboratory summarized the detection algorithms that exploit spectral information and argued that the “apparent” superiority of sophisticated algorithms with simulated data or in laboratory conditions did not necessarily translate to superiority in real-world applications. From the perspective of military defense and reconnaissance applications, they proposed to improve the performance of detection algorithms by solving the problem of the inherent variability target and background spectra (i.e., the mismatch between the spectral library and in-scene signatures) [5,7].

Methods

Results

Discussion

Conclusion