Abstract
With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation.
Highlights
IntroductionConstruction waste refers to waste concrete, waste soil, and waste masonry generated in production, construction, demolition and repair, as well as construction waste generated in engineering due to man-made aspects or for natural reasons [1]
We used the coarse segmentation to separate the large parts of the vegetation and roads from the area and set the segmentation parameters according to the features of different objects
We established the separability index rules for judging the construction waste and buildings, bare soil, and vegetation, and we evaluated the accuracy of the identification results of the construction waste with the accuracy evaluation index of the confusion matrix
Summary
Construction waste refers to waste concrete, waste soil, and waste masonry generated in production, construction, demolition and repair, as well as construction waste generated in engineering due to man-made aspects or for natural reasons [1]. With the acceleration of urbanization in China, the output of construction waste in cities is continuing to increase. Construction waste accounted for 30%~40% of urban waste in 2018 [2]. Due to the generation and accumulation of construction waste, a large number of land resource areas are occupied, which causes air pollution, water pollution, and soil pollution, and the large amount of waste destroys the environment, which human survival relies on [3]. Scientific management of construction waste is one of the important aspects of current
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