Abstract: It is difficult to identify the location, type, area and volume of construction waste piled up illegally in remote sensing images quickly, accurately and effectively. In this paper, based on convolution model, the multi-spectral remote sensing image and panchromatic remote sensing image on its NNDiffuse pan sharpening algorithm fusion processing, it improves the precision of image resolution, in-depth analysis the characteristics of the construction waste pile up some information in remote sensing image. Use DeeplabV3+ network model and the encoder to target the shallow features and high-level semantic feature. From the perspective of image sample data balance adjust the sample weight coefficient to further improve the identification accuracy. Experimental results show that the identification accuracy of construction waste dumps using DeeplabV3+ network reach 82%, which is beneficial to realize the dynamic monitoring and management of construction waste.

Key words: construction waste, remote sensing image, semantic segmentation, DeeplabV3+, image fusion