Incremental Land Cover Classification via Strategies for Edge Removal and Feature Point Aggregation

Convolutional neural networks will face the problem of catastrophic forgetting in the process of incremental learning. To solve this problem, we propose an incremental learning method called the strategy of edge removal and feature point aggregation, or ERFPA for short. In the cross-entropy loss, we perform edge detection and removal on the labels generated by the old model predictions, and then fuse them with the new labels. We calculate the mean point of different classes, and make the model learn features better by narrowing the distance with similar pixels. As demonstrated by the results of our experiment, on two remote sensing image datasets: CCF and Vaihingen, our method achieves state-of-the-art results.