Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

Abstract

Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing imagery tend to be small, however, often consisting of only a handful of images from a single location with a single scale. This limits the models' ability to generalize to other datasets. Domain adaptation has been proposed to improve the models' generalization but we find these approaches are not effective for dealing with the scale variation commonly found between remote sensing image collections. We therefore propose a scale aware adversarial learning framework to perform joint cross-location and cross-scale land-cover classification. The framework has a dual discriminator architecture with a standard feature discriminator as well as a novel scale discriminator. We also introduce a scale attention module which produces scale-enhanced features. Experimental results show that the proposed framework outperforms state-of-the-art domain adaptation methods by a large margin.

arXiv commentThe open-sourced codes are available on Github: https://github.com/xdeng7/scale-aware_da

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• Self-Attention Generative Adversarial Networks
• DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
• Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network
• Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery
• CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing
• Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling