基于增强种子区生长技术的卫星图像监督分类

Astha Baxi, Manoj Pandya, M. Kalubarme, M. Potdar
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引用次数: 4

摘要

遥感图像分类技术已被遥感专家实践,主要采用无监督和有监督两种方法。监督分类需要精确的人工干预来提取特征。增强种子区域生长技术是一种图像分割方法;其中图像像素由使用GPS收集地面真实数据时记录的纬度和经度播种。增强的种子区域生长技术基于8个最近邻像素连接生成聚类。对模式识别标准软件进行相应像素光谱特征的训练。然后使用监督分类算法。该系统可以利用基于位置的服务(LBS)和信息通信技术(ICT)的潜力,使用web服务和网关协议动态地从服务器提取纬度和经度。这种方法从图像中提取特征所需的工作量较小。该方案在印度古吉拉特邦surendranagar地区的卫星图像上进行了应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supervised classification of satellite imagery using Enhanced seeded region growing technique
The image classifications techniques have been practiced by remote sensing experts following certain methods like unsupervised and supervised. Supervised classification requires precise human intervention to extract features. Enhanced Seeded region growing technique is an image segmentation method; where the image pixel is seeded by latitude and longitude recorded during ground truth data collection using GPS. The Enhanced seeded region growing technique generates clusters based upon 8 nearest neighbor pixel connections. Pattern recognition standard software is trained for the spectral signatures of the corresponding pixels. Then the supervised classification algorithm can be used. The system can leverage the potential of Location based services (LBS) and Information Communication Technology (ICT) to dynamically pull the latitude and longitude from the server using web services and gateway protocols. This method requires less effort to extract features from the image. This scheme is applied on satellite imagery covering surendranagar district in Gujarat, India.
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