Affinity propagation for large size hyperspectral image classification

Mariem Soltani; Kacem Chehdi; Claude Cariou

doi:10.1117/12.2028870

17 October 2013 Affinity propagation for large size hyperspectral image classification

Mariem Soltani, Kacem Chehdi, Claude Cariou

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Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 88920P (2013) https://doi.org/10.1117/12.2028870
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

The affinity propagation (AP)¹ is now among the most used methods of unsupervised classification. However, it has two major disadvantages. On the one hand, the algorithm implicitly controls the number of classes from a preference parameter, usually initialized as the median value of the similarity matrix, which often gives over-clustering. On the other hand, when partitioning large size hyperspectral images, its computational complexity is quadratic and seriously hampers its application. To solve these two problems, we propose a method which consists of reducing the number of individuals to be classified before the application of the AP, and to concisely estimate the number of classes. For the reduction of the number of pixels, a pre-classification step that automatically aggregates highly similar pixels is introduced. The hyperspectral image is divided into blocks, and then the reduction step is applied independently within each block. This step requires less memory storage since the calculation of the full similarity matrix is not required. The AP is then applied on the new set of pixels which are then set up from the representatives of each previously formed cluster and non-aggregated individuals. To estimate the number of classes, we introduced a dichotomic method to assess classification results using a criterion based on inter-class variance. The application of this method on various test images has shown that AP results are stable and independent to the choice of the block size. The proposed approach was successfully used to partition large size real datasets (multispectral and hyperspectral images).

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Mariem Soltani, Kacem Chehdi, and Claude Cariou "Affinity propagation for large size hyperspectral image classification", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920P (17 October 2013); https://doi.org/10.1117/12.2028870

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KEYWORDS

Hyperspectral imaging

Image classification

Multispectral imaging

RGB color model

Spatial resolution

Vegetation

Earth observing sensors

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