High-Resolution Remote Sensing Images Change Detection with Siamese Holistically-Guided FCN

Abstract

Change Detection is an important and challenging task in the remote sensing (RS) field, especially when the resolution of RS images is getting higher. The appearance of the deep convolutional neural network (DCNN) provides new opportunities for the high-resolution RS (HRRS) image processing as well as the HRRS CD task. In this paper, we proposed a Siamese holistically-guided FCN (SHG-FCN) model to fully mine the low- and high-level features from HRRS images for completing the CD task. SHG-FCN consists of a Siamese encoder and a holistically-guided decoder. The Siamese encoder adopts five layers of convolution for feature extraction and generates multi-scale difference maps. The decoder employs the holistically-guided architecture, which uses the deep semantic feature as codewords to guide the up-sample of feature map and achieve multi-scale feature fusion. Our model is testified on two public HRRS datasets, and the obtained encouraging CD results illustrate that our method is effective in HRRS CD tasks.