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Urban Material Classification Using Spectral and Textural Features Retrieved from Autoencoders

Ilehag, R.; Leitloff, J.; Weinmann, M.; Schenk, A.

Classification of urban materials using remote sensing data, in particular hyperspectral data, is common practice. Spectral libraries can be utilized to train a classifier since they provide spectral features about selected urban materials. However, urban materials can have similar spectral characteristic features due to high inter-class correlation which can lead to misclassification. Spectral libraries rarely provide imagery of their samples, which disables the possibility of classifying urban materials with additional textural information. Thus, this paper conducts material classification comparing the benefits of using close-range acquired spectral and textural features. The spectral features consist of either the original spectra, a PCA-based encoding or the compressed spectral representation of the original spectra retrieved using a deep autoencoder. The textural features are generated using a deep denoising convolutional autoencoder. The spectral and textural features are gathered from the recently published spectral library KLUM. Three classifiers are used, the two well-established Random Forest and Support Vector Machine classifiers in addition to a Histogram-based Gradient Boosting Classification Tree. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000125305
Veröffentlicht am 27.10.2020
DOI: 10.5194/isprs-annals-V-1-2020-25-2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2194-9042
KITopen-ID: 1000125305
Erschienen in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-1-2020, 2020, XXIV ISPRS Congress (2020 edition). Ed.: N. Paparoditis
Verlag ISPRS
Seiten 25-32
Serie ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 5
Schlagwörter Material classification, Spectral features, Textural features, Autoencoder, Compressed representation, Spectral library
Nachgewiesen in Scopus
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