Impact of Learned Domain Specific Compression on Satellite Image Object Classification

Abstract

This paper proposes a methodology for learned compression for satellite imagery. The proposed method utilizes an image patching and stitching approach to address the high resolution of satellite images. We present rate-distortion metrics showing that this methodology outperforms JPEG2000, currently used on satellites. In addition, we demonstrate that using satellite images to train the compression model leads to superior performance compared to using non-domain-specific data. Furthermore, a detailed evaluation of the compression algorithm in a downstream classification task is conducted. The results demonstrate that 77.83% classification accuracy is still achievable for highly compressed images with a bitrate of 0.02 BPPs when the classification model is trained on images from the same compression model. The downstream classification task evaluation highlights that the performance of the classification model is highly dependent on the type of compression applied to the training data. When trained with learned compression images, the model can only classify images with an acceptable level of accuracy (>77%) if they had also undergone learned compression. Likewise, a model trained with JPEG images can only classify JPEG images with acceptable accuracy (>89%).