TY - GEN
T1 - Remote sensing image classification by mean shift and color quantization
AU - Taud, Hind
AU - Couturier, Stéphane
AU - Carrillo-Rivera, José Joel
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Classification
KW - Color quantization
KW - Forest mapping
KW - Integral image
KW - Landsat
KW - Mean shift
KW - Multispectral imaging
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=84875662950&partnerID=8YFLogxK
U2 - 10.1117/12.974427
DO - 10.1117/12.974427
M3 - Contribución a la conferencia
AN - SCOPUS:84875662950
SN - 9780819492777
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Image and Signal Processing for Remote Sensing XVIII
T2 - Image and Signal Processing for Remote Sensing XVIII
Y2 - 24 September 2012 through 26 September 2012
ER -