A Refined Method of High-Resolution Remote Sensing Change Detection Based on Machine Learning for Newly Constructed Building Areas

Automatic detection of newly constructed building areas (NCBAs) plays an important role in addressing issues of ecological environment monitoring, urban management, and urban planning. Compared with low-and-middle resolution remote sensing images, high-resolution remote sensing images are superior in spatial resolution and display of refined spatial details. Yet its problems of spectral heterogeneity and complexity have impeded research of change detection for high-resolution remote sensing images. As generalized machine learning (including deep learning) technologies proceed, the efficiency and accuracy of recognition for ground-object in remote sensing have been substantially improved, providing a new solution for change detection of high-resolution remote sensing images. To this end, this study proposes a refined NCBAs detection method consisting of four parts based on generalized machine learning: (1) pre-processing; (2) candidate NCBAs are obtained by means of bi-temporal building masks acquired by deep learning semantic segmentation, and then registered one by one; (3) rules and support vector machine (SVM) are jointly adopted for classification of NCBAs with high, medium and low confidence; and (4) the final vectors of NCBAs are obtained by post-processing. In addition, area-based and pixel-based methods are adopted for accuracy assessment. Firstly, the proposed method is applied to three groups of GF1 images covering the urban fringe areas of Jinan, whose experimental results are divided into three categories: high, high-medium, and high-medium-low confidence. The results show that NCBAs of high confidence share the highest F1 score and the best overall effect. Therefore, only NCBAs of high confidence are considered to be the final detection result by this method. Specifically, in NCBAs detection for three groups GF1 images in Jinan, the mean Recall of area-based and pixel-based assessment methods reach around 77% and 91%, respectively, the mean Pixel Accuracy (PA) 88% and 92%, and the mean F1 82% and 91%, confirming the effectiveness of this method on GF1. Similarly, the proposed method is applied to two groups of ZY302 images in Xi'an and Kunming. The scores of F1 for two groups of ZY302 images are also above 90% respectively, confirming the effectiveness of this method on ZY302. It can be concluded that adoption of area registration improves registration efficiency, and the joint use of prior rules and SVM classifier with probability features could avoid over and missing detection for NCBAs. In practical applications, this method is contributive to automatic NCBAs detection from high-resolution remote sensing images.

Automated summary

Automatic detection of newly constructed building areas using high-resolution remote sensing images is essential for ecological environment monitoring and urban planning. The proposed refined detection method based on machine learning shows promising results in accurately identifying and classifying NCBAs with high confidence.