Stacked Encoder-Decoders for Accurate Semantic Segmentation of Very High Resolution Satellite Datasets

Semantic segmentation is currently a mainstream method for addressing several remote sensing applications, achieving recently remarkable performance by employing deep learning techniques. In particular, this is the case for pixel-wise dense classification models in very high resolution remote sensing datasets. In this paper, we exploit the use of a relatively deep architecture based on repetitive downscale-upscale processes that had been previously employed for human pose estimation tasks. By integrating such a model, we are aiming to capture and extract low-level details, such as small objects, object boundaries and edges. Experimental results and quantitative evaluation has been performed on the publicly available ISPRS (WGIII4) benchmark dataset indicating the potential of the proposed approach.