| 多特征流形鉴别嵌入的高分辨率遥感影像分类 |
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| 引用本文： | 杨素妨,杜林.多特征流形鉴别嵌入的高分辨率遥感影像分类[J].测绘通报,2021,0(9):37-42. |
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| 作者姓名： | 杨素妨 杜林 |
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| 作者单位： | 百色学院, 广西 百色 533000 |
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| 基金项目： | 广西自然科学基金（2017GXNSFAA198746）；广西高校中青年教师基础能力提升项目（2017KY0738） |
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| 摘 要： | 针对高分辨遥感影像同谱异物、同物异谱导致单一特征分类结果精度较差的问题，本文提出了多特征流形鉴别嵌入的高分辨率遥感影像分类方法。该方法首先提取高分辨率影像数据的光谱特征与LBP纹理特征；然后通过样本数据的联合光谱、纹理特征的空间距离及对应的类别信息，构建影像对象的类间图与类内图，用于学习高分辨率影像上的鉴别流形结构，保证在嵌入空间上尽可能不同地物特征分离、相同地物特征紧聚，确保相同地物光谱、纹理特征的相似性，完成光谱、纹理鉴别特征的有效提取，以充分挖掘影像特征，有效提高影像的分类精度。在GF-2遥感数据集上进行试验，结果表明本文算法可实现多特征的有效融合，分类精度均优于传统方法，可达93.41%。
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| 关 键 词： | 高分辨率遥感影像 光谱特征 纹理特征 流形学习 |
| 收稿时间： | 2020-09-23 |
High-resolution remote sensing image classification based on multi-feature popular discrimination embedding |
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| YANG Sufang,DU Lin.High-resolution remote sensing image classification based on multi-feature popular discrimination embedding[J].Bulletin of Surveying and Mapping,2021,0(9):37-42. |
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| Authors: | YANG Sufang DU Lin |
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| Institution: | Baise University, Baise 533000, China |
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| Abstract: | In order to solve the problem of poor accuracy of single feature classification caused by the "same spectrum foreign body, same body foreign spectrum" of high-resolution remote sensing image, a high-resolution remote sensing image classification method based on multi-feature manifold discrimination embedding is proposed in this paper. Firstly, the spectral features and LBP texture features of high-resolution image data are extracted. Then, through the joint spectrum of sample data, the spatial distance of texture features and the corresponding category information, the inter class and intra class graphs of image objects are constructed to learn the discriminative manifold structure on high-resolution images, so as to ensure that the features of different features in the embedded space are separated as far as possible, the same ground features are closely clustered to ensure the similarity of spectral and texture features of the same ground features, and complete the effective extraction of spectral and texture identification features, so as to fully mine image features and effectively improve the classification accuracy of images. Experimental results on GF-2 remote sensing data set show that the proposed algorithm can effectively fuse multiple features, and the classification accuracy can reach 93.41%, which is better than the traditional methods. |
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| Keywords: | high-resolution remote sensing image spectral feature texture feature manifold learning |
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