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

Abstract

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.

A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

“…In recent years, computer vision techniques have become continuously more important to support a wide range of application areas, including foreground detection and/or background modeling for anti-intrusion and monitoring systems [1][2][3][4][5], object detection and target recognition for security and observation systems [6][7][8][9][10], human action and behavior recognition [11][12][13][14][15][16], assessment of progress of motor impairments by vision-based rehabilitation systems able to analyze body movements over time [17][18][19], or to support human-centered interfaces to drive advanced devices [20][21][22]. One of the application areas where computer vision has grown the most is without doubt the development of vision systems based on UAVs.…”