Forest remote sensing change detection provides an important technical support for forest management decisions and analysis of forest disturbance factors. However, lack of data in specialized fields leads to the detection accuracy improved difficulty. In this study, a forest remote sensing change detection model in the context of few-shot learning is proposed. The proposed model achieves end-to-end change detection algorithm for forest scenes from two perspectives of data augmentation and updated few-shot algorithm. Firstly, forest fragments images are jointly generated by using feature extraction network and generative adversarial network. Then, forest fragments are blended into the original change detection dataset by Poisson blending method to achieve effective augmentation. Furthermore, the end-to-end change detection network is also updated using a few-shot learning. Additionally, Meta-learning module is added to the slow feature analysis algorithm based on the multi-attention mechanism to realize the detection effect improvements. The proposed model improves the F1 score from 86% to 91% on the two datasets. Moreover, it increases the F1 score by 6.52% on average.

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.