English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several citymodelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areasis required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However,results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remainlimited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution usingsuperspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materialsclassification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectancespectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using thisdata set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental methodand then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relyingon a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectrafor several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability wassimulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering theclassification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify commonurban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) tomaterial classification was also shown

SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES