Building change detection on high-resolution imagery with a multi-task semantic change detection method

Huan Liu; Xiang Liao; Zhipan Wang; Zhifei Li; Hua Zhang

doi:10.1117/12.2681303

8 June 2023 Building change detection on high-resolution imagery with a multi-task semantic change detection method

Huan Liu, Xiang Liao, Zhipan Wang, Zhifei Li, Hua Zhang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270717 (2023) https://doi.org/10.1117/12.2681303
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Building change detection from high-resolution images plays an essential part in landcover classification and the analysis of urban spatial information. With the development of deep learning (DL) in remote sensing, DL has become the mainstream method in the building change detection field. However, the current building change detection methods still only can detect building change, but few studies investigate whether the building is increasing or decreasing, in other words, they can only capture the spatial change of buildings, but can’t get the temporal change of buildings. To solve this problem, this manuscript proposes a new simple but effective multi-task learning building change detection method, which can capture building increase or decrease on high-resolution images at the same time. First, we modify the DenseNet121 as our backbone to extract deep semantic features, mainly because DenseNet has a strong feature extraction ability, but the computation cost is relatively low. Second, we design a differential structure to detect building increases or decreases at the same time with a new loss function, namely Tanimoto loss. A large image with very high resolution is selected to verify the effectiveness and robustness of our method. On the one hand, the result indicates that our proposed building change detection method can detect building increases or decreases accurately, and the quantitative precision shows that the F1-score is over 80%. On the other hand, this paper presents model design idea for building change detection model, which can provide several novel perspectives to other research fields, such as object detection on high-resolution images, change detection of other objects.

Citation Download Citation

Huan Liu, Xiang Liao, Zhipan Wang, Zhifei Li, and Hua Zhang "Building change detection on high-resolution imagery with a multi-task semantic change detection method", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270717 (8 June 2023); https://doi.org/10.1117/12.2681303

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KEYWORDS

Object detection

Semantics

Deep learning

RGB color model

Image segmentation

Remote sensing

Education and training

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