Classification of hyperspectral images based on weighted DMPs

Date

2012-07-27

Author

Ulusoy, İlkay
MURA, Mauro Dalla

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This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size and contrast characteristics of the structures in an image. Due to the wide variety of objects present in a scene, the pixels belonging to the same semantic structure may not have homogeneous spatial and spectral features. In addition, instead of a single peak (which can be related to a measure of the scale), multiple local maxima and multiple responses are usually observed in the DMP. In order to handle such intra-class variability, class-specific weighting functions are employed in order to differently modulate the DMP values according to the different characteristics of the land cover types. In such way, it is possible to differentiate the behaviors of the DMP for each pixel in the image according to its semantic, providing an increase of the separability of the classes. At first, a DMP computed with opening by reconstruction (DMPO) and one with closing by reconstruction (DMPC) are derived on each of the first principle components extracted from the hyperspectral image. Then, both profiles are weighted by each class-specific weighting function and concatenated in a single data structure. The constructed feature vectors are considered by a random forest classifier.

Subject Keywords

Hyperspectral images, Differential Morphological Profiles, Classification

URI

https://hdl.handle.net/11511/42581

DOI

https://doi.org/10.1109/igarss.2012.6351697

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

İ. Ulusoy and M. D. MURA, “Classification of hyperspectral images based on weighted DMPs,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42581.