Homogeneous Aggregation Convolution for Building Extraction From Remote Sensing Images

Abstract

An increasing number of convolutional neural networks are being applied to various fields, and they have achieved excellent performance. However, standard convolution cannot recognize the connection between surrounding features and cannot characterize them efficiently. For example, buildings in urban remote sensing images exhibit geometric changes, such as rotation, scaling, and local changes. The concept of dynamic convolution is proposed to solve the aforementioned problem. Existing dynamic convolution methods enhance an expression by dynamically changing the sampling points or weights of convolution. However, these end-to-end training methods do not consider which sampling points are important. In this letter, we propose a homogeneous aggregation convolution (HAC) that gives more attention to the sampling points that belong to the same class as the target point. A generate probability map module is designed to generate a probability map between target and sampling points and share this probability map across convolution layers to save computational cost. Experimental results demonstrate that the proposed HAC outperforms standard convolution, and the intersection over union and F1 score are higher than the standard convolution by 2.23% and 1.28%, respectively, on the WHU and Austin datasets. Compared with other convolutions, the proposed HAC convolution is the most efficient in building extraction.