Remote Sensing Change Detection Based on Unsupervised Multi-Attention Slow Feature Analysis

Remote Sensing

Jian Wang¹  Shengjia Cui²  Weipeng Jing³  Peilun Kang³  Guangsheng Chen³  Songyu Zhu³  Houbing Song⁴ 

[1] Aerospace Information Research Institute, Beijing 100094, China;Baidu Company Ltd., Beijing 100085, China;College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China;Department of Electrical, Computer, Software and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA;

关键词: remote sensing;    unsupervised change detection;    deep learning;    slow feature analysis;    multi-attention;   

DOI  :  10.3390/rs14122834

来源: DOAJ

【 摘 要 】

With the development of big data, analyzing the environmental benefits of transportation systems by artificial intelligence has become a hot issue in recent years. The ground traffic changes can be overlooked from a high-altitude perspective, using the technology of multi-temporal remote sensing change detection. We proposed a novel unsupervised algorithm by combining the image transformation and deep learning method. The new algorithm for remote sensing images is named multi-attention slow feature analysis (ASFA). In this model, three parts perform different functions respectively. The first part records to the K-BoVW to classify the categories of the ground objects as a channel parameter. The second part is a residual convolution with multiple attention mechanisms including temporal, spatial, and channel attention. Feature extraction and updating are completed at this link. Finally, we put the updated features in the slow feature analysis to highlight the variant components which we want and then generate the change map visually. Experiments on three very high-resolution datasets verified that the ASFA has a better performance than four basic change detection algorithms and an improved SFA algorithm. More importantly, this model works well for traffic road detection and helps us analyze the environmental benefits of traffic changes.

【 授权许可】

Unknown