A Pixel Texture Index Algorithm and Its Application

Xiaodan Sun, Xiaofang Sun
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Abstract

Image segmentation is essential for object-oriented analysis, and classification is a critical parameter influencing analysis accuracy. However, image classification and segmentation based on spectral features are easily perturbed by the high-frequency information of a high spatial resolution remotely sensed (HSRRS) image, degrading its classification and segmentation quality. This article first presents a pixel texture index (PTI) by describing the texture and edge in a local area surrounding a pixel. Indeed.. The experimental results highlight that the HSRRS image classification and segmentation quality can be effectively improved by combining it with the PTI image. Indeed, the overall accuracy improved from 7% to 14%, and the kappa can be increased from 11% to 24%, respectively.
像素纹理指数算法及其应用
图像分割对于面向对象的分析至关重要,而分类是影响分析精度的关键参数。然而,基于光谱特征的图像分类和分割很容易受到高空间分辨率遥感(HSRRS)图像高频信息的干扰,从而降低其分类和分割质量。本文首先通过描述像素周围局部区域的纹理和边缘,提出了像素纹理指数(PTI)。确实如此。实验结果表明,通过将 HSRRS 图像与 PTI 图像相结合,可以有效提高 HSRRS 图像的分类和分割质量。事实上,整体准确率从 7% 提高到 14%,卡帕值从 11% 提高到 24%。
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