Extraction and Analysis of Urban Vegetation Information Based on Remote Sensing Image

Zhu, Yongmei; Yang, Kun; Pan, E'mei; Yin, Xiaoxue; Zhao, Juchao

doi:10.1109/geoinformatics.2018.8557075

Cited by 1 publication

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“…We calculated spectral indices (hereafter RGB indices) from the RGB bands usually referred as pseudo-vegetation indices to help in discriminating non-vegetation objects from vegetation; furthermore, to emphasize the contrast between vegetation and ground [68]. High reflectivity in the visible green band with lower intensity in the red and blue band refers to vegetation [69][70][71]. Inversely, at the non-vegetation points red and blue bands have the highest reflectance (e.g., roads, buildings) [72,73] (Table 1).…”

Section: Rgb Indicesmentioning

confidence: 99%

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

et al. 2020

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Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

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