ESDSCNet: an enhanced shallow feature difference and semantic context network for remote sensing change detection: with building change detection as a case

ABSTRACT

Change detection (CD) is a challenging task considering land surface cover changes. Recently, deep learning has been introduced into CD. However, these methods suffer from shortcomings such as insufficient utilization of shallow features and inadequate aggregation of semantic context, which result in pseudo changes and poor integrity of change objects. To address this, an enhanced shallow feature difference and semantic context network (ESDSCNet) is proposed in this study for remote sensing CD. It uses HRNet to extract multi-scale features from bi-temporal images to obtain shallow and deep features. To fully exploit the shallow features, they are input into the difference statistical texture learning (DSTL) module to extract more discriminative features. Subsequently, the features enhanced by DSTL are fed into the change object contextual representation (COCR) module along with the deep difference features extracted by HRNet to characterize the contextual information of the change object. To verify the performance of ESDSCNet in different scenes, this paper takes building change detection as a case and conducts experiments based on three datasets. The experimental results reveal that ESDSCNet outperforms six other advanced methods, regardless of the intersection over union or the harmonic mean of precision rate and recall rate, which further confirms the effectiveness of the proposed network. In addition, another speciality of this paper is that ESDSCNet can identify not only ‘Where has changed’ but also ‘What has changed what’, so it has potential application in semantic change detection besides building change detection.

KEYWORDS:

• bi-temporal images
• building change detection
• fully convolutional Siamese network
• difference statistical texture learning
• change object contextual representations

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM under Grant [2022-02-14] and the Fundamental Research Funds for the Central Universities.