Convolutional neural network-based remote sensing image building change detection method

Abstract

The invention provides a convolutional neural network-based remote sensing image building change detection method, and the method comprises the following steps: 1, building a sample library accordingto an existing image and a surface coverage vector file, wherein the sample library comprises a building classification data set and a building change detection simulation data set; 2, training a building extraction network (Mask R-CNN or a multi-scale full convolutional network) by using the building classification data set in the sample library constructed in the step 1, learning building characteristics in the high-resolution remote sensing image, and then extracting a multi-temporal building binary classification graph of the change area by using the trained network; 3, training a change detection network Mask CD-net by using the analog data set in the sample library constructed in the step 1; directly using the Mask CD-net to predict the building change detection data set to obtain achange detection result. If the real change data set is contained, the Mask CD-net is further trained and refined, a better change detection effect is obtained.

Description

technical field [0001] The invention relates to a deep learning method for detecting building changes in high-resolution remote sensing images, and in particular proposes a self-trainable change detection network. This method can be used for building change detection in two-phase remote sensing images. Background technique [0002] Change detection refers to the process of identifying differences in the state of an object or phenomenon by observing the same area at different times. Change detection frameworks use multitemporal datasets to qualitatively analyze the temporal effects of phenomena and quantify changes. In remote sensing applications, changes are viewed as changes in surface composition with different rates. Land cover and land use change information is so important that it has practical uses in various applications such as GIS database updates, urban development trends, natural hazard assessment and forest fires, logging, etc. [0003] Change detection analys...

AI Technical Summary

Problems solved by technology

At the same time, the network trains itself by simulating the parallax changes of buildings, which solves the problem of complete registration of buildings on high-resolution aerial images from different perspectives.

Images