Paint detection in shortwave and midwave hyperspectral using one dimensional CNN and guided grad-CAM band selection

Byungjin Kang; Sungho Kim; Jungsub Shin; Sun Ho Kim

doi:10.1117/12.2587813

12 April 2021 Paint detection in shortwave and midwave hyperspectral using one dimensional CNN and guided grad-CAM band selection

Byungjin Kang, Sungho Kim, Jungsub Shin, Sun Ho Kim

Author Affiliations +

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 1172712 (2021) https://doi.org/10.1117/12.2587813
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Short wave infrared (SWIR) is the reflected radiance in the 1-2.5 μm range. Mid wave infrared (MWIR) is the radiated radiance in the 3-5 μm range. This paper proposes a paint detection method using two infrared bands with different characteristics. Object detection is one of the issues in hyperspectral image (HSI). We use one dimensional convolution neural network (1D-CNN) and guided gradient-weighted class activation mapping (Guided Grad-CAM) for band selection. We make a 1D-CNN architecture and select bands using Guided Grad- CAM from well-trained 1D-CNN. Finally, paint is detected using selected bands. We use datasets included short wave infrared band (SWIR) and mid wave infrared band (MWIR).

Conference Presentation

Citation Download Citation

Byungjin Kang, Sungho Kim, Jungsub Shin, and Sun Ho Kim "Paint detection in shortwave and midwave hyperspectral using one dimensional CNN and guided grad-CAM band selection", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 1172712 (12 April 2021); https://doi.org/10.1117/12.2587813

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