Building detection in VHR remote sensing images using a novel dual attention residual-based U-Net (DAttResU-Net): An application to generating building change maps

In today's era, increasing access to very high-resolution remote sensing images (VHR-RSIs) has enhanced building detection and change assessment capabilities. These applications provide accurate urban mapping, facilitate effective land management, and support disaster assessment by delivering detailed insights into building structures and their temporal changes. This study uses a two-stage process to present a pioneering approach for generating precise building maps (BMs) and subsequent building change maps (BCMs) from VHR-RSIs. The primary question addressed by the research is how to enhance the U-Net architecture to improve its sensitivity to both high-level semantic features (HLSF) and low-level spatial features (LLSF) in the building detection task. For this purpose, in the initial stage of the method, a novel deep learning model called dual attention residual-based U-Net (DAttResU-Net) is introduced. This model incorporates two significant modifications to the conventional U-Net, enhancing its capacity to yield bi-temporal BMs. Firstly, each standard convolutional block (CB) is replaced with an optimized CB incorporating a channel-spatial attention module attuned to the building objects' crucial HLSF. Secondly, an additional attention module is integrated into the encoder-decoder path of the model, heightening the sensitivity of U-Net to vital LLSF of buildings while disregarding extraneous background spatial information during the fusion of HLSF and LLSF. In the subsequent stage, the bi-temporal BMs generated by the DAttResU-Net are subjected to a box-based class-object change detection methodology to produce accurate BCMs. The effectiveness of the proposed architecture is rigorously evaluated against state-of-the-art models in both BM and BCM generation contexts, utilizing the well-established WHU dataset for experimentation. The experimental results indicated that the DAttResU-Net model, boasting an average of $P_{\text{FN}}$/ $P_{\text{FP}}$ value of 2.33/1.34 (%) surpasses the performance of the state-of-the-art models in generating bi-temporal BMs. Furthermore, the building change detection outcomes demonstrated the proficient role of the bi-temporal BMs predicted by the proposed model in leading to the most optimal BCMs, exhibiting average $P_{\text{FN}}$/ $P_{\text{FP}}$ value of 2.63/8.93 (%), outperforming comparative networks. Finally, we concluded that the proposed DAttResU-Net architecture is a highly promising and applicable model for producing reliable BMs and BCMs.