Multibranch Network for Addressing Intraclass Variation in Remote Sensing Building Detection

Abstract

This article presents multibranch network architecture for addressing the problem of large intraclass variation in building detection task. Previous methods solved the problem by learning single structured and shared feature space with regularization. However, we reveal that the feature sharing strategy is less advantageous at deeper layers. We have analyzed the channel-wise contribution of the deep features for recognizing individual buildings and find that the feature space is separated into several clusters, among which the discriminative features are not shared much. Based on the analysis, we propose a multibranch neural network that solves the problem by decomposing a building class into subclasses and learning specialized feature space for each subclass. The proposed model is demonstrated on two remote sensing building detection benchmarks, where the model outperforms the state-of-the-art segmentation models and the previous techniques for addressing the large intraclass variation.