Unsupervised hierarchical partitioning of hyperspectral images: application to marine algae identification

B. Chen; K. Chehdi; E. De Oliveria; C. Cariou; B. Charbonnier

doi:10.1117/12.2194419

15 October 2015 Unsupervised hierarchical partitioning of hyperspectral images: application to marine algae identification

B. Chen, K. Chehdi, E. De Oliveria, C. Cariou, B. Charbonnier

Author Affiliations +

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 96430M (2015) https://doi.org/10.1117/12.2194419
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

In this paper a new unsupervised top-down hierarchical classification method to partition airborne hyperspectral images is proposed. The unsupervised approach is preferred because the difficulty of area access and the human and financial resources required to obtain ground truth data, constitute serious handicaps especially over large areas which can be covered by airborne or satellite images.

The developed classification approach allows i) a successive partitioning of data into several levels or partitions in which the main classes are first identified, ii) an estimation of the number of classes automatically at each level without any end user help, iii) a nonsystematic subdivision of all classes of a partition P_j to form a partition P_j+1, iv) a stable partitioning result of the same data set from one run of the method to another.

The proposed approach was validated on synthetic and real hyperspectral images related to the identification of several marine algae species. In addition to highly accurate and consistent results (correct classification rate over 99%), this approach is completely unsupervised. It estimates at each level, the optimal number of classes and the final partition without any end user intervention.

Citation Download Citation

B. Chen, K. Chehdi, E. De Oliveria, C. Cariou, and B. Charbonnier "Unsupervised hierarchical partitioning of hyperspectral images: application to marine algae identification", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430M (15 October 2015); https://doi.org/10.1117/12.2194419

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