基于频谱和光谱特征的高光谱地物分类比较  

A comparative analysis on hyperspectral land-cover classification based on frequency spectrum and spectral characteristics

作　　者：张莹[1] 张晓丽[1,2,3] 李宏志[1] 刘会玲[1] Zhang Ying;Zhang Xiaoli;Li Hongzhi;Liu Huiling(College of Forestry,Beijing Forestry University,Beijing 100083,China;Beijing Key Laboratory of Precision Forestry,Beijing Forestry University,Beijing 100083,China;Key Laboratory for Forest Silviculture and Conservation of Ministry of Education,Beijing Forestry University,Beijing 100053,China)

机构地区：[1]北京林业大学林学院,北京100083 [2]北京林业大学精准林业北京市重点实验室,北京100083 [3]北京林业大学省部共建森林培育与保护重点实验室,北京100083

出　　处：《北京林业大学学报》2018年第7期1-8,共8页Journal of Beijing Forestry University

基　　金："863"国家高技术研究发展计划项目"数字化森林资源监测关键技术研究"(2012AA102001)

摘　　要：【目的】频谱作为物质的能量特征进行地物的识别是可行的。本文针对国内外利用频谱进行地物分类尤其是森林植被分类研究的匮乏,探索频谱的地物识别潜力,将高光谱影像的光谱曲线转化为频谱进行地物识别研究。【方法】以福建将乐林场为研究区,利用国产环境小卫星高光谱影像(HJ-1A HSI)和同时成像的多光谱影像(CCD),通过能量分离变换的方法对高光谱和多光谱进行融合,获取高空间分辨率的高光谱影像;然后,将融合影像的光谱曲线转化到频率域,进而获取频谱;通过"频谱距离"对研究区进行地物分类,并将分类结果与光谱角填图(SAM)分类结果进行比较。【结果】在频域中植被类别和非植被类别的低阶幅度谱具有明显的可分性,频谱方法提高了马尾松、杉木和阔叶林的制图精度,对于光谱特征相似的不同森林植被具有更好的区分能力;非植被类别在1阶谐波的频谱容易区分,植被类别需要用前7次谐波的幅度谱进行区分,随着频率的增大,频谱变化趋于相似,并且非植被类别在频域的能量累计速度高于植被类别;与SAM的分类结果比较发现,基于频谱的分类方法总体分类精度为84.19%,比SAM分类结果总体精度提高0.7%。【结论】利用频谱信息可以降低光谱曲线上噪声的影响,保留类别的重要区别信息,提高地类的分类精度,因此利用频域中的频谱进行地类识别具有可行性。[Objective] Frequency spectrum as an energy feature of matter can be used to differentiate ground object recognition. However,the study for classification by frequency spectrum is scanty at home and abroad,especially for forest vegetation classification research. This study explored the potential of frequency spectrum in identifying the objects by converting optical spectrum of image into frequency spectrum. [Method]For exploring frequency spectrum in land-cover classification,the optical spectrum was converted to frequency spectrum to classify based on the fused hyperspectral images from domestic HJ-1 A HSI and CCD data by CN Spectral Sharpening in farmland of Jiangle County,Sanming City ofFujian Province,southern China. Then the distance was developed to differentiate the objects based on the obtained frequency spectrum. The classification result based on frequency spectrum was compared with spectral angle mapping（ SAM） based on spectrum space. [Result] The study result showed that the separability was clear among frequency spectrum of different ground objects. The frequency spectrum of the vegetation was significantly different with frequency spectrum of non-vegetation classed,and the spectrum of different tree species can also be differentiated in the low frequency region. The precisions for Pinus massoniana,Cunninghamia lanceolata and broadleaved forest were improved based on the FSSM method. The non-vegetation classes can be distinguished using frequency spectrum of first order harmonic. The vegetation classes can be distinguished using frequency spectrum from one to seven harmonic. However,the frequency spectrum for all objects tends to be similar with the increase of frequency. And the energy accumulated speed was faster for non-vegetation classes. Compared with SAM result,the overall classification precision from frequency spectrum method increased by 0. 7%,which was 84. 19%. [Conclusion] The comparison results from frequency spectrum of different land types and classification results indicated that

关 键 词：高光谱遥感 频谱分析 离散傅立叶变换 地物分类 

分 类 号：S771.8[农业科学—森林工程]