Remote sensing image classification by mean shift and color quantization

Remote sensing imagery involves large amounts of data acquired by several kinds of airborne, sensors, wavelengths spatial resolutions, and temporal frequencies. To extract the thematic information from this data, many algorithms and techniques for segmentation and classification have been proposed. The representation of the different multispectral bands as true or false color imaging has been widely employed for visual interpretation and classification. On the other hand, the color quantization, which is a well-known method for data compression, has been utilized for color image segmentation and classification in computer vision application. The number of colors in the original image is reduced by minimizing the distortion between the quantified and the original image with the aim of conserving the pattern representation. Considering the density estimation in the color or feature space, similar samples are grouped together to identify patterns by any clustering techniques. Mean shift algorithm has been successfully applied to different applications as the basis for nonparametric unsupervised clustering techniques. Based on an iterative manner, mean shift detects modes in a probability density function. In this article, the contribution consists in providing an unsupervised color quantization method for image classification based on mean shift. To avoid its high computational cost, the integral image is used. The method is evaluated on Landsat satellite imagery as a case study to underline forest mapping. A comparison between the proposed method and the simple mean shift is carried out. The results prove that the proposed method is useful in multispectral remote sensing image classification study. © 2012 SPIE.