Change detection based on Faster R-CNN for high-resolution remote sensing images

ABSTRACT

Change detection is of great significance in remote sensing. The advent of high-resolution remote sensing images has greatly increased our ability to monitor land use and land cover changes from space. At the same time, high-resolution remote sensing images present a new challenge over other satellite systems, in which time-consuming and tiresome manual procedures must be needed to identify the land use and land cover changes. In recent years, deep learning (DL) has been widely used in the fields of natural image target detection, speech recognition, face recognition, etc., and has achieved great success. Some scholars have applied DL to remote sensing image classification and change detection, but seldomly to high-resolution remote sensing images change detection. In this letter, faster region-based convolutional neural networks (Faster R-CNN) is applied to the detection of high-resolution remote sensing image change. Compared with several traditional and other DL-based change detection methods, our proposed methods based on Faster R-CNN achieve higher overall accuracy and Kappa coefficient in our experiments. In particular, our methods can reduce a large number of false changes.

Acknowledgments

The authors would like to acknowledge the funding from the LIESMARS Special Research Funding. The authors would like to acknowledge the funding from the Fundamental Research Funds for the Central Universities. The authors would also like to thank the developers in the CDTStudio, GDAL, QGIS, Keras and Theano developer communities for their open source projects.

Additional information

Funding

This work is supported in part by the funding from the LIESMARS Special Research Funding and in part by the Fundamental Research Funds for the Central Universities.