Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-08, Vol.15 (15), p.3799
• Main Authors: Luo, Jianhui, Chen, Qiang, Wang, Lei, Huang, Yixiao
• Format: Article
• Language: English
• Subjects: Ablation; Algorithms; Analysis; Attention; Change detection; Classification; Computer vision; Datasets; False alarms; Feature extraction; Image resolution; Image segmentation; multi-difference image fusion; Neural networks; Orientation behavior; Performance enhancement; Remote sensing; Researchers; Robustness; Salience; Vector analysis; very high resolution (VHR); visual attention model; Visual perception; Wavelet transforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15153799

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.