Neural Disparity Computation from IKONOS Stereo Imagery in the Presence of Occlusions

摘要

In computer vision, stereoscopic image analysis is a well-known technique capable of extracting the third (vertical) dimension. Starting from this knowledge, the Remote Sensing (RS) community has spent increasing efforts on the exploitation of Ikonos one-meter resolution stereo imagery for high accuracy 3D surface modelling and elevation data extraction. In previous works our team investigated the potential of neural adaptive learning to solve the correspondence problem in the presence of occlusions. In this paper we present an experimental evaluation of an improved version of the neural based stereo matching method when applied to Ikonos one-meter resolution stereo images affected by occlusion problems. Disparity maps generated with the proposed approach are compared with those obtained by an alternative stereo matching algorithm implemented in a (non-)commercial image processing software toolbox. To compare competing disparity maps, quality metrics recommended by the evaluation methodology proposed by Scharstein and Szelinski (2002, IJCV, 47, 7-42) are adopted.