光学学报, 2014, 34 (9): 0901004, 网络出版: 2014-08-15   

边缘修正CV模型的卫星遥感云图分割方法

Satellite Remote Sensing Cloud Image Segmentation Using Edge Corrected CV Model
作者单位
1 南京航空航天大学电子信息工程学院, 江苏 南京 210016
2 南京信息工程大学气象灾害省部共建教育部重点实验室, 江苏 南京 210044
摘要
对卫星遥感云图进行自动分割是分析卫星云图资料的重要步骤。为了更加准确的对卫星遥感云图进行分割,提出了融合边缘信息CV模型的卫星遥感云图分割方法。对原卫星云图进行扩散,得到平滑图像,根据平滑图像计算边缘信息,将得到的边缘信息融入CV模型中,并加入距离规范项使得CV模型的水平集函数在演化过程中不需要重新初始化。实验结果表明,与传统CV模型、区域能量拟合水平集模型、偏置场修正水平集模型相比,所提方法分割出的云区域更加准确,分割速度更快。
Abstract
Segmenting satellite remote sensing cloud images is an essential step of analyzing satellite cloud image data. In order to segment satellite remote sensing cloud images more accurately, a satellite remote sensing cloud image segmentation method based on Chan Vese (CV) model incorporating edge information is proposed. Satellite cloud image is diffused and a smooth image is obtained. The edge information is calculated based on the smooth image. The edge information is incorporated into the CV model, and a distance regularized term is added to avoid the reinitialization of the level set function during its evolution. Experimental results show that, compared with conventional CV model, region-scalable fitting energy level set model and bias field correction level set model, the proposed method can segment region of cloud more accurately and the speed is faster.
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宋昱, 吴一全, 毕硕本. 边缘修正CV模型的卫星遥感云图分割方法[J]. 光学学报, 2014, 34(9): 0901004. Song Yu, Wu Yiquan, Bi Shuoben. Satellite Remote Sensing Cloud Image Segmentation Using Edge Corrected CV Model[J]. Acta Optica Sinica, 2014, 34(9): 0901004.

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