改进LBP和HSV颜色直方图相结合的地表状态识别

doi:10.13474/j.cnki.11-2246.2020.0040

测绘通报 ›› 2020, Vol. 0 ›› Issue (2): 29-36,71.doi: 10.13474/j.cnki.11-2246.2020.0040

改进LBP和HSV颜色直方图相结合的地表状态识别

孙艺珊^1,2, 李晓洁^1,3, 赵凯^1,3

1. 中国科学院东北地理与农业生态研究所, 吉林长春 130102;
2. 中国科学院大学, 北京 100049;
3. 长春净月潭遥感实验站, 吉林长春 130102

收稿日期:2019-10-18 出版日期:2020-02-25 发布日期:2020-03-04
作者简介:孙艺珊(1994-),女,硕士生,主要研究方向为近地表遥感图像识别。E-mail:sunyishan@iga.ac.cn
基金资助:
国家自然科学基金面上项目（41671350）；吉林省科技人才项目（20170520090JH）

A combined algorithm of improved LBP and HSV for surface state recognition

SUN Yishan^1,2, LI Xiaojie^1,3, ZHAO Kai^1,3

1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Changchun Jingyuetan remote Sensing Experimental Station, Changchun 130102, China

Received:2019-10-18 Online:2020-02-25 Published:2020-03-04

摘要/Abstract

摘要： 在利用近地表遥感探测方法对地表信息进行反演时，地表状态的不同会导致电磁波传播方式不同，进而决定了所采取的遥感探测方式、探测波长及探测手段的不同。因此，地表状态的识别是进行近地表遥感反演的前提和基础，其识别的准确率也决定了反演结果的准确度。本文提供了一种改进LBP（local binary pattern）和HSV颜色直方图相结合的地表状态识别算法，通过改进阈值的LBP算法和HSV颜色直方图提取特征向量并建立判别条件，最后利用K邻近算法对测试样本与训练样本进行特征匹配，得到识别结果。将野外采集的411张图片多粗随机分组为训练样本和测试样本，结果表明206张测试样本的分类正确率都达98.7%以上，证明了本文算法的有效性。

关键词: 图像识别, 改进LBP, HSV颜色特征, K邻近分类, 地表图像

Abstract: Different surface states will lead to different modes of electromagnetic wave propagation, which determines the different detection methods, detection wavelengths and detection methods of remote sensing, when the surface information is retrieved by near-surface remote sensing. Therefore, surface state recognition is the premise of remote sensing retrieval of near-surface, and recognition accuracy determines remote sensing retrieval accuracy. A combined algorithm of LBP(local binary pattern)and HSV color histogram for surface state recognition is proposed. Feature vector extraction is carried out by combining the Improved Threshold LBP algorithm and HSV color histogram, then discriminant condition is established, in the end, the results of recognition can be realized by feature matching between test samples and training samples, based on K-neighborhood algorithm. 411 images collected in the field are divided into training samples and test samples randomly. The correct rate of recognition for 206 test samples is more than 98.7%, It is proved that the algorithm is effective.

Key words: image recognition, improved LBP, HSV color feature, K-neighborhood classification, surface image

中图分类号:

P237

孙艺珊, 李晓洁, 赵凯. 改进LBP和HSV颜色直方图相结合的地表状态识别[J]. 测绘通报, 2020, 0(2): 29-36,71.

SUN Yishan, LI Xiaojie, ZHAO Kai. A combined algorithm of improved LBP and HSV for surface state recognition[J]. Bulletin of Surveying and Mapping, 2020, 0(2): 29-36,71.

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改进LBP和HSV颜色直方图相结合的地表状态识别

A combined algorithm of improved LBP and HSV for surface state recognition

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