Class Activation Map-Based Data Augmentation for Satellite Smoke Scene Detection

Abstract

With global warming, increasingly severe forest fires have caused serious damage to the Earth’s ecological environment. Remote sensing (RS) technology has become an important means of forest fire monitoring owing to its unique advantages of multiple cycles and wide coverage. In smoke scene detection, the current simple mixed sample data augmentation (MSDA) methods lead to the loss of key objects in images, and training the samples whose images do not match the labels is the main reason for the decline in classifier accuracy. We propose a Class Activation Map-based Mixing method (CAMMix), a new MSDA method. In contrast to previous data augmentation (DA) methods, CAMMix can select the region and degree of mixing throughout the significance map. With the aid of an auxiliary classifier, CAMMix generates a mixed mask with class significance, such that the newly generated mixed sample distribution is close to the original data distribution. We also propose an intervention for the coverage method to further prevent the loss of smoke objects. On a smoke dataset (USTC_SmokeRS), CAMMix achieves the best accuracy of 94.95% and 83% for 64% and 8% training set respectively. It results that CAMMix and variants provides high classification accuracy and preserves key object information, outperforming the Mixup, CutMix, ResizeMix and FMix.