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Image Analysis Based On Cognitive Color Attributes For Classification of Environmental Remote Sensing Scenes

This paper presents a supervised classification approach based on the analysis of color tones depicted by objects in scenes with natural targets. In some environmental classification problems, the existing classes occur in subtle color variations, leading to a limited and inefficient set of samples collected by the user. In the proposed framework, a reduced Hue-Saturation-Value (HSV) color system is used to select appropriate shades of color standing for classes of interest in a supervised fashion. The classification strategy is designed to emulate decisions made by the analyst providing high level of generalization in problems where classes occur in many color tones of similar hue. An experiment performed with Amazon rainforest scene including deforestation activities was classified using the proposed technique and several well-known classification approaches. The method proved to be useful for noncomplex problems, also providing simplicity, intuitiveness, and the ability to generalize the training process.