Speckle aware spatial search based segmentation algorithm for crop classification in SAR images using a three component K-NN model

IF 1.4 4区 地球科学 Q4 ENVIRONMENTAL SCIENCES
Chandran Bipin, Chandu Venkateswara Rao, Padavala Veera Sridevi
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引用次数: 0

Abstract

We provide a speckle aware image segmentation algorithm for synthetic aperture radar (SAR) data. It uses search based segmentation using a three-component machine learning model where speckle noise is considered as discrete component of the feature description. This method allows for the removal of the need for a de-speckling filter during the feature extraction process for SAR images, resulting in a more efficient and accurate approach. A three-component model is used to efficiently represent a feature in SAR data. The algorithm is used to segment different crops from Sentinel-1 C-band SAR data. We describe the search-based segmentation algorithm, three-component model, and its design using K-NN algorithm. We tested the proposed algorithm against K-NN based segmentation on Sentinel-1 images de-speckled using widely used Lee, Refine Lee, Frost, and Gamma-MAP filters. The proposed method is found to produce better classification accuracy compared to results from K-NN and commonly used de-speckling filters.
基于三分量K-NN模型的SAR图像作物分类分割算法
提出了一种适合合成孔径雷达(SAR)数据的斑点感知图像分割算法。它使用基于搜索的分割,使用三分量机器学习模型,其中散斑噪声被认为是特征描述的离散分量。该方法允许在SAR图像的特征提取过程中去除对去斑点滤波器的需要,从而产生更有效和准确的方法。采用三分量模型有效地表示SAR数据中的特征。该算法用于从Sentinel-1 c波段SAR数据中分割不同作物。介绍了基于搜索的分割算法、三分量模型,并采用K-NN算法进行了设计。我们使用广泛使用的Lee、Refine Lee、Frost和Gamma-MAP滤波器对Sentinel-1图像进行了基于K-NN的分割测试。与K-NN和常用的去斑点滤波器的结果相比,该方法具有更好的分类精度。
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来源期刊
Journal of Applied Remote Sensing
Journal of Applied Remote Sensing 环境科学-成像科学与照相技术
CiteScore
3.40
自引率
11.80%
发文量
194
审稿时长
3 months
期刊介绍: The Journal of Applied Remote Sensing is a peer-reviewed journal that optimizes the communication of concepts, information, and progress among the remote sensing community.
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