Gated Feature Aggregation for Height Estimation From Single Aerial Images

Abstract

Height estimation from single images, strictly speaking, is an ill-posed problem. However, recently, it is shown that it is both possible and feasible to learn a mapping from image statistics to height information. In spite of recent efforts in this field, how to learn fine-shape preserving features, such as object boundaries and contours, is still an open issue. In this work, we propose a progressive learning network to estimate height information from single aerial images in a coarse-to-fine manner. In particular, a gated feature aggregation module is introduced to effectively combine low-level and high-level features. The proposed method is validated on three public datasets, including the Vaihingen dataset, the Potsdam dataset, and the DFC2019 dataset. Both quantitative and qualitative experimental results demonstrate that the proposed method can achieve more accurate height estimation from single aerial images, especially with better object boundary and contour preserving capability, than four related height estimation methods.