Research on Remote Sensing Feature Selection and Information Extraction of Urban Construction Waste Based on Jilin-1 Image

Abstract

Construction waste generally refers to the waste generated by man-made or natural factors such as new construction, demolition, and repair. It has many characteristics such as discrete distribution, various types, and frequent changes. Conventional survey methods are difficult to obtain construction waste location information quickly and accurately, while information extraction methods based on high-resolution remote sensing images can efficiently acquire construction waste information in a large area. Therefore, this paper uses the domestic Jilin-1 satellite (JL-101A) remote sensing image, based on multi-scale segmentation, and through the method of object-oriented remote sensing feature recognition. This paper regards the Qianxinzhuang Village in Daxing District, Beijing as the study area. And it takes research on information extraction of construction waste in cities. The results show that the optimal segmentation scale of the study area is 74; the overall accuracy of the object-oriented knowledge rule classification method is 88.0%, and the Kappa coefficient is 83.6%. The results show that the object-oriented method based on the domestic Jilin-1 satellite is feasible for information extraction of construction waste, and it has certain significance for the management of construction waste.