Abstract
Abstract. The development and increase of multi and hyperspectral sensors in the recent years have significantly improved urban structure analysis and interpretation. The current study is the first to investigate the potential of DESIS hyperspectral images for the detection or identification of urban roof materials. After field campaigns in 2014, 2015 and 2018 to collect ground truth points and rooftops radiometric properties; a linear spectral mixture, implemented using a non-negative least squares (NNLS) regression based on the sequential coordinate-wise algorithm (SCA) was applied on a DESIS image from 2020 of Kigali city to identify the different rooftops material and color. Although results show that most endmembers were predicted with a very low probability, the study proved that the combination of spectral mixture and hyperspectral data such as DESIS have great potential in the detection of rootops material. The presented study also highlghted a number of challenges resulting from the choice of spectral mixture algorithm and colinearity between materials.
Highlights
Human activities, since their beginnings, have left an undeniable footprint on the World; Especially cities are among the most noticeable activities(UN, 2014)
The aim of this study is to demonstrate the capabilities of DESIS data for the spectral unmixing of urban surfaces
The “mesma” library of the “RStoolbox” is implemented using a non-negative least squares (NNLS) regression based on the sequential coordinate-wise algorithm (SCA)
Summary
Since their beginnings, have left an undeniable footprint on the World; Especially cities are among the most noticeable activities(UN, 2014). Almost 60 percent of the world’s population live in urban areas, a proportion that is expected to increase to 66 by 2050. This development underlines the need of sustainable urban planning which prepares cities for challenges related to overpopulation, the supply of infrastructures, teaching and medical services, and the creation of housing space (UN, 2014). Building materials are an indicator of the socio-economic status but satellite images of very high-resolution (VHR) are needed to achieve the desired quality and detail (Ye et al, 2017). Most VHR imaging satellites only have limited numbers of spectral bands, making the identification of materials impossible. Hyperspectral satellites operate with a significantly higher number of bands, but at the cost of spatial resolution
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