Remote Sensing Applications and Decision Support

High- and low-level hierarchical classification algorithm based on source separation process

[+] Author Affiliations
Mohamed Anis Loghmari, Emna Karray

Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, Laboratoire de Télédétection et Systèmes d’Information à Référence Spatiale, Rue Béchir Salem Belkhiria Campus universitaire, BP 37, Le Bélvédère, Tunis 1002, Tunisia

Université de Tunis El Manar, Institut Supérieur d’Informatique d’El Manar, 2 Rue Abou Raihane Bayrouni, Ariana 2080, Tunisia

Mohamed Saber Naceur

Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, Laboratoire de Télédétection et Systèmes d’Information à Référence Spatiale, Rue Béchir Salem Belkhiria Campus universitaire, BP 37, Le Bélvédère, Tunis 1002, Tunisia

J. Appl. Remote Sens. 10(4), 046022 (Dec 05, 2016). doi:10.1117/1.JRS.10.046022
History: Received January 6, 2016; Accepted November 14, 2016
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Abstract.  High-dimensional data applications have earned great attention in recent years. We focus on remote sensing data analysis on high-dimensional space like hyperspectral data. From a methodological viewpoint, remote sensing data analysis is not a trivial task. Its complexity is caused by many factors, such as large spectral or spatial variability as well as the curse of dimensionality. The latter describes the problem of data sparseness. In this particular ill-posed problem, a reliable classification approach requires appropriate modeling of the classification process. The proposed approach is based on a hierarchical clustering algorithm in order to deal with remote sensing data in high-dimensional space. Indeed, one obvious method to perform dimensionality reduction is to use the independent component analysis process as a preprocessing step. The first particularity of our method is the special structure of its cluster tree. Most of the hierarchical algorithms associate leaves to individual clusters, and start from a large number of individual classes equal to the number of pixels; however, in our approach, leaves are associated with the most relevant sources which are represented according to mutually independent axes to specifically represent some land covers associated with a limited number of clusters. These sources contribute to the refinement of the clustering by providing complementary rather than redundant information. The second particularity of our approach is that at each level of the cluster tree, we combine both a high-level divisive clustering and a low-level agglomerative clustering. This approach reduces the computational cost since the high-level divisive clustering is controlled by a simple Boolean operator, and optimizes the clustering results since the low-level agglomerative clustering is guided by the most relevant independent sources. Then at each new step we obtain a new finer partition that will participate in the clustering process to enhance semantic capabilities and give good identification rates.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Mohamed Anis Loghmari ; Emna Karray and Mohamed Saber Naceur
"High- and low-level hierarchical classification algorithm based on source separation process", J. Appl. Remote Sens. 10(4), 046022 (Dec 05, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046022


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