Understanding deep learning decision for satellite image classification

Asif Mehmood

doi:10.1117/12.2591974

12 April 2021 Understanding deep learning decision for satellite image classification

Asif Mehmood

Proceedings Volume 11735, Pattern Recognition and Tracking XXXII; 117350C (2021) https://doi.org/10.1117/12.2591974
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper present class activation mapping in conjunction with transfer learning to investigate and explain the predictions of a deep learning based satellite image classification. Deep learning based classifiers offer no way of gauging what a network has learned or which part of an input to the network was responsible for the prediction of the network. When these models fail and give incorrect predictions, they often fail spectacularly without any warning or explanation. The presented class activation mapping (CAM) technique can address this issue and provide the visual explanations of the predictions of convolutional neural networks. In addition, the proposed method can also reveal what specific part of an input image can confuse the network resulting an incorrect prediction. The proposed approach employing transfer learning using ImageNet pretrained models is implemented on the xView dataset. The experimental results are very promising and provide an insight into the satellite based image classification.

Conference Presentation

Citation Download Citation

Asif Mehmood "Understanding deep learning decision for satellite image classification", Proc. SPIE 11735, Pattern Recognition and Tracking XXXII, 117350C (12 April 2021); https://doi.org/10.1117/12.2591974

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