Deep learning

Geology

Transfer of learning

Pattern recognition (psychology)

Feature (linguistics)

Mathematics

Gene

Pixel

Mathematical analysis

Philosophy

Artificial intelligence

Generalization

Chemistry

Change detection

Computer science

Linguistics

Feature extraction

Biochemistry

Remote sensing

Robustness (evolution)

Convolutional neural network

IEEE Transactions on Geoscience and Remote Sensing

Change detection based on remote sensing (RS) images has a wide range of applications in many fields. However, many existing approaches for detecting changes in RS images with complex land covers still have room for improvement. In this article, a high-resolution RS image change detection approach based on a deep feature difference convolutional neural network (CNN) is proposed. This approach uses a CNN to learn the deep features from RS images and then uses transfer learning to compose a two-channel network with shared weight to generate a multiscale and multidepth feature difference map for change detection. The network is trained by a change magnitude guided loss function proposed in this article and needs to train with only a few pixel-level samples to generate change magnitude maps, which can help to remove some of the pseudochanges. Finally, the binary change map can be obtained by a threshold. The approach is tested on several data sets from different sensors, including WorldView-3, QuickBird, and Ziyuan-3. The experimental results show that the proposed approach achieves better performance compared with other classic approaches and has fewer missed detections and false alarms, which proves that the proposed approach has strong robustness and generalization ability.

A Feature Difference Convolutional Neural Network-Based Change Detection Method