Neural network based SOM for multispectral image segmentation in RGB and HSV color space

Ganesan, Khamar Basha Shaik, B. Sathish, V. Kalist
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引用次数: 4

Abstract

Segmentation is the process of partitioning an image into number of meaningful images as segments or clusters. The segmentation is initial but important process which is used to locate boundaries and objects in images. This paper is concerned with segmentation of color satellite images using neural network based kohonen's self-organizing maps. This unsupervised competitive network is used to visualize and interpret large data sets. In this paper, test images are segmented in RGB and HSV color space using self-organizing map and the segmentation results are compared using error image, peak signal to noise ratio, and execution time. The efficiency of proposed method is tested with Landsat and Terra (MODIS sensor) satellite images.
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基于神经网络的RGB和HSV色彩空间多光谱图像分割
分割是将图像分割成若干有意义的图像作为段或簇的过程。分割是一个初始的重要过程,用于定位图像中的边界和目标。本文研究了基于kohonen自组织图的神经网络对彩色卫星图像的分割。这种无监督竞争网络用于可视化和解释大型数据集。本文采用自组织映射在RGB和HSV色彩空间对测试图像进行分割,并利用误差图像、峰值信噪比和执行时间对分割结果进行比较。利用Landsat和Terra (MODIS传感器)卫星图像验证了该方法的有效性。
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