Robust building footprint extraction from big multi-sensor data using deep competition network

TL;DR

This paper introduces a deep competition network that effectively fuses optical images and LiDAR data to improve building footprint extraction accuracy from complex multi-sensor datasets.

Contribution

The study presents a novel deep superpixelwise convolutional encoder-decoder architecture specifically designed for robust building footprint extraction from multi-sensor remote sensing data.

Findings

DCN achieves competitive accuracy compared to existing deep segmentation models.

The model demonstrates robustness across diverse building scenes in large multi-sensor datasets.

Fusion of optical and LiDAR data enhances extraction performance.

Abstract

Building footprint extraction (BFE) from multi-sensor data such as optical images and light detection and ranging (LiDAR) point clouds is widely used in various fields of remote sensing applications. However, it is still challenging research topic due to relatively inefficient building extraction techniques from variety of complex scenes in multi-sensor data. In this study, we develop and evaluate a deep competition network (DCN) that fuses very high spatial resolution optical remote sensing images with LiDAR data for robust BFE. DCN is a deep superpixelwise convolutional encoder-decoder architecture using the encoder vector quantization with classified structure. DCN consists of five encoding-decoding blocks with convolutional weights for robust binary representation (superpixel) learning. DCN is trained and tested in a big multi-sensor dataset obtained from the state of Indiana in the United States with multiple building scenes. Comparison results of the accuracy assessment showed that DCN has competitive BFE performance in comparison with other deep semantic binary segmentation architectures. Therefore, we conclude that the proposed model is a suitable solution to the robust BFE from big multi-sensor data.