{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:33:47Z","timestamp":1768530827271,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs13010158","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T21:18:57Z","timestamp":1609881537000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5720-2338","authenticated-orcid":false,"given":"Qiang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Qianhao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-0530","authenticated-orcid":false,"given":"Jinfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography, Western University, London, ON N6A5C2, Canada"}]},{"given":"Mingyi","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5473-8495","authenticated-orcid":false,"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.wasman.2020.09.030","article-title":"Challenges in current construction and demolition waste recycling: A China study","volume":"118","author":"Ma","year":"2020","journal-title":"Waste Manag."},{"key":"ref_2","first-page":"22","article-title":"Status Quo and Development Analysis of Resource Utilization of Construction Waste in China","volume":"19","author":"Lan","year":"2018","journal-title":"Jiangxi Build. Mater."},{"key":"ref_3","unstructured":"Ding, Z.L. (2018). Construction waste reduction and resource utilization technology. JuShe, 72."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.wasman.2020.09.004","article-title":"Transition to circular economy in the construction industry: Environmental aspects of waste brick recycling scenarios","volume":"118","year":"2020","journal-title":"Waste Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.wasman.2020.08.006","article-title":"Waste generation, composition, and handling in building-related construction and demolition in Hanoi, Vietnam","volume":"117","author":"Hoang","year":"2020","journal-title":"Waste Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"054012","DOI":"10.1088\/1748-9326\/ab7b99","article-title":"Using remote sensing to detect, validate, and quantify methane emissions from California solid waste operations","volume":"15","author":"Cusworth","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_7","first-page":"792","article-title":"Construction and application of knowledge decision tree after a disaster for water body information extraction from remote sensing images","volume":"22","author":"Chen","year":"2018","journal-title":"J. Remote Sens."},{"key":"ref_8","first-page":"215","article-title":"New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)","volume":"78","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21036","DOI":"10.1109\/ACCESS.2020.2969812","article-title":"Vegetation land use\/land cover extraction from high-resolution satellite images based on adaptive context inference","volume":"8","author":"Zhan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xin, J., Zhang, X.C., Zhang, Z.Q., and Fang, W. (2019). Road extraction of high-resolution remote sensing images derived from denseUNet. Remote Sens., 11.","DOI":"10.3390\/rs11212499"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8314","DOI":"10.1029\/2018GL077605","article-title":"Recent acceleration of a rock glacier complex, \u00c1djet, norway, documented by 62 years of remote sensing observations","volume":"45","author":"Eriksen","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1002\/gj.3061","article-title":"An integrated approach for extraction of lithology information using the SPOT 6 imagery in a heavily Quaternary-covered region\u2014North Baoji District of China","volume":"53","author":"Han","year":"2018","journal-title":"Geol. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3578","DOI":"10.1109\/JSTARS.2019.2929514","article-title":"Unsupervised Change Detection in Multispectral Remote Sensing Images via Spectral-Spatial Band Expansion","volume":"12","author":"Liu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s11430-019-9547-x","article-title":"Advances in urban information extraction from high-resolution remote sensing imagery","volume":"63","author":"Gong","year":"2020","journal-title":"Sci. China Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11831-017-9239-y","article-title":"Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: A Review","volume":"26","author":"Thyagharajan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yao, X.D., Yang, H., Wu, Y.L., Wu, P.H., Wang, B., Zhou, X.X., and Wang, S. (2019). Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features. Sensors, 19.","DOI":"10.3390\/s19122792"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, L.J., Zhang, G.M., Wang, Z.Y., Liu, J.G., Shang, J.L., and Liang, L. (2019). Bibliometric analysis of remote sensing research trend in crop growth monitoring: A case study in China. Remote Sens., 11.","DOI":"10.3390\/rs11070809"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Derakhshan, S., Cutter, S.L., and Wang, C. (2020). Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery. Remote Sens., 12.","DOI":"10.3390\/rs12050895"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/LGRS.2019.2915315","article-title":"Lateral-Slice Sparse Tensor Robust Principal Component Analysis for Hyperspectral Image Classification","volume":"17","author":"Sun","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","first-page":"1","article-title":"Remote Sensing Image Enhancement Via Edge-Preserving Multiscale Retinex","volume":"11","author":"Teng","year":"2019","journal-title":"IEEE Photon. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","article-title":"Edge-Enhanced GAN for Remote Sensing Image Superresolution","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1109\/TIP.2018.2885490","article-title":"A Fast Image Dehazing Algorithm Using Morphological Reconstruction","volume":"28","author":"Botella","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s12524-019-00962-2","article-title":"Image Processing Techniques Applied to Satellite Data for Extracting Lineaments Using PCI Geomatica and Their Morphotectonic Interpretation in the Parts of Northwestern Himalayan Frontal Thrust","volume":"47","author":"Pandey","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11220-018-0196-9","article-title":"Spectral\u2013Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction","volume":"19","author":"Tu","year":"2018","journal-title":"Sens. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bachagha, N., Luo, L., Wang, X., Masini, N., Moussa, T., Khatteli, H., and Lasaponara, R. (2020). Mapping the Roman Water Supply System of the Wadi el Melah Valley in Gafsa, Tunisia, Using Remote Sensing. Sustainability, 12.","DOI":"10.3390\/su12020567"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1007\/s40899-020-00425-4","article-title":"Surface water detection and delineation using remote sensing images: A review of methods and algorithms","volume":"6","author":"Bijeesh","year":"2020","journal-title":"Sustain. Water Resour. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1007\/s12524-018-0798-7","article-title":"Efficiency Evaluating of Automatic Lineament Extraction by Means of Remote Sensing (Case Study: Venarch, Iran)","volume":"46","author":"Sharifi","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, S., Tang, L., Hupy, J.P., Wang, Y., and Shao, G.F. (2020). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res., 1\u20136.","DOI":"10.1007\/s11676-020-01155-1"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2006.01.003","article-title":"Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction","volume":"101","author":"Jiang","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1049\/iet-ipr.2018.6440","article-title":"Combining colour and grey-level co-occurrence matrix features: A comparative study","volume":"13","author":"Khaldi","year":"2019","journal-title":"IET Image Process."},{"key":"ref_31","first-page":"313","article-title":"Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images","volume":"23","author":"Zhang","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5749","DOI":"10.4028\/www.scientific.net\/AMR.518-523.5749","article-title":"Statistical Class Feature in Texture Analysis of Remote Sensing Imagery","volume":"518","author":"Teng","year":"2012","journal-title":"Adv. Mater. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/83.469936","article-title":"Texture classification and segmentation using wavelet frames","volume":"4","author":"Unser","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5109","DOI":"10.1158\/0008-5472.CAN-20-1231","article-title":"Integrating Mathematical Modeling with High-Throughput Imaging Explains How Polyploid Populations Behave in Nutrient-Sparse Environments","volume":"80","author":"Kimmel","year":"2020","journal-title":"Cancer Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, H.M., Yao, R., Xu, L., Yuan, Y., Li, G.Y., and Deng, W. (2018). Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy. Entropy, 20.","DOI":"10.3390\/e20090682"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4518","DOI":"10.1109\/JSTARS.2020.3015049","article-title":"A Contextual Bidirectional Enhancement Method for Remote Sensing Image Object Detection","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.1109\/JSTARS.2018.2817121","article-title":"High-Resolution Remote Sensing Image Change Detection by Statistical-Object-Based Method","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","first-page":"269","article-title":"Automated IFC-based building information modelling and extraction for supporting value analysis of buildings","volume":"20","author":"Zhang","year":"2020","journal-title":"Int. J. Constr. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Balboa, J.L., Alba-Fern\u00e1ndez, M.V., Ariza-L\u00f3pez, F.J., and Rodr\u00edguez-Avi, J. (2018). Analysis of Thematic Similarity Using Confusion Matrices. Isprs Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060233"},{"key":"ref_40","first-page":"698","article-title":"Deep metric learning method for high resolution remote sensing image scene classification","volume":"48","author":"Ye","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_41","first-page":"785","article-title":"Recognition of earthquake-induced landslide and spatial distribution patterns triggered by the Jiuzhaigou earthquake in August 8, 2017","volume":"23","author":"Li","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s13755-020-0100-6","article-title":"Constructing a knowledge-based heterogeneous information graph for medical health status classification","volume":"8","author":"Pham","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, R.S., Bilal, M., Yang, X.C., Wang, J.X., and Li, W. (2018). Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050181"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/158\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:07:18Z","timestamp":1760159238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,5]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010158"],"URL":"https:\/\/doi.org\/10.3390\/rs13010158","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,5]]}}}