| 引用本文: | 赵 巍,崔淑梅,吴 锐,刘家锋.多／高光谱 遥感数据的类立体纹理特征[J].哈尔滨工业大学学报,2012,44(5):86.DOI:10.11918/j.issn.0367-6234.2012.05.017 |
| ZHAO Wei,CUI Shu-mei,WU Rui,LIU Jia-feng.Study on quasi-3-dimensional texture features for multi-, hyper-spectral remote sensing data analysis[J].Journal of Harbin Institute of Technology,2012,44(5):86.DOI:10.11918/j.issn.0367-6234.2012.05.017 |
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| 摘要: |
| 鉴于多／高光谱遥感数据同源同点多波段同时获取的特点，提出了基于灰度级差关联概率矩阵
( Gray Level Difference Associated Possibility matrix．GLDAP)的视觉差异分析方法，以有效地利用图像底层数
据及数据之间的相关性．根据地物的波谱特性，统计两波段图像灰度协同变化的规律并记录在GLDAP矩阵
中，基于此矩阵提取了遥感数据的类立体纹理特征．将该方法与灰度共生矩阵( GLCM)纹理分析方法的遥感
地物分类性能比较，实验结果表明：基于GLDAP的纹理提取及分析表现出良好的性能，3种地物分类效果
明显优于GLCM方法，能够减少因单波段中地物可分性差而导致的误识，克服了GLCM方法对图像统计描述
的局限性，在相同时间开销下GLDAP右法较GLCM有更优的解译分析精度 |
| 关键词: 遥感图像解译 多／高光谱数据 地物提取 类立体纹理 灰度级差关联概率矩阵 |
| DOI：10.11918/j.issn.0367-6234.2012.05.017 |
| 分类号:TP751 |
| 基金项目:哈尔滨工业大学科研创新基金资助项目( HIT
NSRIF．2010040) |
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| Study on quasi-3-dimensional texture features for multi-, hyper-spectral remote sensing data analysis |
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ZHAO Wei1, CUI Shu-mei2, WU Rui1, LIU Jia-feng1
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1.School of Computer Science and Technology, Harbin Institute of Technology, 150001 Harbin, China;2.School of Electrical Engineering and Automation, Harbin Institute of Technology, 150001 Harbin, China
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| Abstract: |
| According to the synchronous acquirement of multi-, hyper-spectral remote sensed imagery, a Gray
Level Difference Associated Possibility matrix ( GLDAP) method is proposed in the paper to analyze visual
differences between multi-band data. The matrix is built on two bands of image that are selected in light of
land-cover spectrum characteristics. Thereafter, the co-varying statistics of gray level in each image is recor-
ded and quasi-3-dimention texture features are extracted based on GLDAP. During experiments, GLDAP is
employed in classifications and annotations of land cover types, compared with GLCM method. The results re-
veal that the GLDAP has better performances than GLCM. Moreover, it could overcome the limitation of single
band processing and understanding, on which GLCM based, and to a certain degree, decrease misrecognition
rate caused by worse visual discrimination of land types at data level. The two methods have same time com-
plexity; hence, GLDAP may be accepted as another choice in getting excellent precision and better perform-
ance under the same time consuming |
| Key words: remote sensing image interpretation multi-hyper-spectral data land cover extraction quasi-3-
dimensional textures gray level difference associated possibility matrix |
