Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection
This addresses the need for efficient change detection in earth observation by reducing reliance on labeled data, though it is incremental as it builds on existing unsupervised methods.
The paper tackles the problem of optical aerial image change detection by proposing an unsupervised self-training algorithm (USTA) that avoids costly manual labeling, achieving competitive performance on real datasets.
Optical aerial images change detection is an important task in earth observation and has been extensively investigated in the past few decades. Generally, the supervised change detection methods with superior performance require a large amount of labeled training data which is obtained by manual annotation with high cost. In this paper, we present a novel unsupervised self-training algorithm (USTA) for optical aerial images change detection. The traditional method such as change vector analysis is used to generate the pseudo labels. We use these pseudo labels to train a well designed convolutional neural network. The network is used as a teacher to classify the original multitemporal images to generate another set of pseudo labels. Then two set of pseudo labels are used to jointly train a student network with the same structure as the teacher. The final change detection result can be obtained by the trained student network. Besides, we design an image filter to control the usage of change information in the pseudo labels in the training process of the network. The whole process of the algorithm is an unsupervised process without manually marked labels. Experimental results on the real datasets demonstrate competitive performance of our proposed method.