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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