Analysis of spectral characteristics of typical construction waste

Jiayuan Zhang

doi:10.1117/12.3033601

13 June 2024 Analysis of spectral characteristics of typical construction waste

Jiayuan Zhang

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131802H (2024) https://doi.org/10.1117/12.3033601
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

With the acceleration of urban construction and renovation, the volume of construction waste has significantly increased, accounting for 30% to 40% of the total urban solid waste emissions. Therefore, analyzing the spectral characteristics of typical construction waste is crucial for remote sensing identification and monitoring, providing essential ground verification information. This study conducted spectral data collection of construction waste in areas such as Daxing District of Beijing and Pingdingshan City, Henan. Various spectral analysis methods were applied to examine its characteristics. The findings revealed that: (1) the wavelength range is between 1913nm and 1932nm, the spectral curves of all kinds of construction wastes have obvious reflection troughs, In the range of 1660-1690nm, organic building materials exhibit absorption valleys. (2) In the wavelength intervals of 600~1100nm and 1860~1930nm, spectral curves exhibit consistent trends, rendering them unsuitable for discrimination. (3) In the range of 1900~2200nm, samples exhibit reduced susceptibility to the spectral characteristics of the dust-proof net, closely mirroring the spectral features of uncovered samples, thereby rendering it suitable as a basis for classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiayuan Zhang "Analysis of spectral characteristics of typical construction waste", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131802H (13 June 2024); https://doi.org/10.1117/12.3033601

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