IMPROVING PAIRWISE DSM WITH 3SGM: A SEMANTIC SEGMENTATION FOR SGM USING AN AUTOMATICALLY REFINED NEURAL NETWORK

Abstract

The amount of very high resolution optical satellite images at our disposal is continuously increasing. Besides, associated satellite programs often come with high revisit rates and geometric properties that allow for either opportunistic or by-design 3D stereo reconstruction. Digital Surface Models (DSM) computed from these satellite images offer new possibilities. In the past, the high revisit rate has largely benefited glacier monitoring studies. Now, DSM with increased resolution provided on urban areas can be used for smart city applications as well. However, most of these require 3D modeling of buildings with level of details ranging from 0 to 2. This is where the need for better reconstructed buildings inside DSM arises. Indeed, building edges and corners tend to be smoothed and softened by the stereo matching step of a DSM computation pipeline. This undesired behavior can mostly be linked to the difficult task of optimizing the Disparity Space Image, thus finding good balance between smoothing untextured areas while conserving sharp discontinuities where needed. In this paper, we show how the optimization can benefit from an input building semantic segmentation. We also provide a method to create it from a very high satellite image in epipolar geometry using a convolutional neural network. To help our network generalize well on unseen areas we propose an interactive learning method based on clicked annotations. Eventually, we show that annotations can be automatically created, hence removing the need for an operator and making our solution suitable for operational conditions.