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Communication Dans Un Congrès Année : 2016

A sun/shadow approach for the classification of hyperspectral data

Résumé

Shadows constitute a major issue for various remote sensing applications, from un-mixing and anomaly detection to image segmentation and classification. It is particularly problematic in urban areas, characterized by a high heterogeneity in terms of both relief, with the presence of numerous tall buildings, and materials, which implies that a single shadow can hide a wide variety of different objects. Shadow induces a strong weakening of the signal which can lead, in the context of a classification process, to the creation of a parasit shadow class for unsupervised cases, or to an increase of class variability for supervised ones. This paper introduces a new classification algorithm where sunlit and shadowed pixels are processed separately. Using a shadow mask, sunlit pixels are isolated and classified using a SVM or a Kmeans algorithm. Then, a centroid is processed for each classe and used by a spectral angle mapper (SAM) method to classify the shadowed pixels. This approach showed promissing results, especially in an unsupervised context.
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Dates et versions

hal-01385498 , version 1 (24-10-2016)

Identifiants

  • HAL Id : hal-01385498 , version 1

Citer

G. Roussel, C. Ceamanos, X. Briottet, C. Weber. A sun/shadow approach for the classification of hyperspectral data. 16th Onera-DLR Aerospace Symposium (ODAS 2016), Jun 2016, OBERPFAFFENHOFEN, Germany. ⟨hal-01385498⟩
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