Use of Commercial Satellite Imagery to Monitor Changing Arctic Polygonal Tundra

Abstract

Commercial satellite sensors offer the luxury of mapping of individual permafrost features and their change over time. Deep learning convolutional neural nets (CNNs) demonstrate a remarkable success in automated image analysis. Inferential strengths of CNN models are driven primarily by the quality and volume of hand-labeled training samples. Production of hand-annotated samples is a daunting task. This is particularly true for regional-scale mapping applications, such as permafrost feature detection across the Arctic. Image augmentation is a strategic "data-space" solution to synthetically inflate the size and quality of training samples by transforming the color space or geometric shape or by injecting noise. In this study, we systematically investigate the effectiveness of a spectrum of augmentation methods when applied to CNN algorithms to recognize ice-wedge polygons from commercial satellite imagery. Our findings suggest that a list of augmentation methods (such as hue, saturation, and salt and pepper noise) can increase the model performance.