论文信息 - Classification of remotely sensed images using clonal selection theory of Artificial Immune System

Classification of remotely sensed images using clonal selection theory of Artificial Immune System

In general, this paper deals with Image Processing using Metaheuristics Optimization Algorithms (IP-MOA). We are focused on supervised classification of remotely sensed images using a clonal selection theory of an Artificial Immune System (AIS). We shall propose a comparative study between the maximum likelihood (MLLH) classifier which is statistical and probabilistic approach and artificial immune system (AIS) which is a bio-inspired approach and commonly named “metaheuristcs”. The most motivations to explore this new kind of approaches for data classification are also presented. MLLH and AIS are applied to classify a multispectral image acquired on June 2001 by ETM+ sensor of Landsat-7 satellite. This multi-band image covers a northeastern part of Algiers (Algeria). From obtained results, we concluded that AIS approach may present a promising metaheuristic classifier for data classification.

Radja Kheddam | Aichouche Belhadj-Aissa | A. B. Aissa | R. Khedam

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