CAJ | 학술논문

为了准确掌握半干旱地区的土地利用情况,以辽宁省彰武县为研究区,分析多时相的 HJ-1 B遥感影像。依据影像各时相的归一化植被指数(NDVI)及其波谱特性,获得对影像数据的整体把握。提出基于 NDVI、光谱和纹理信息相结合的最大似然分类方法,通过此方法探讨该区的土地利用信息,并将分类结果与基于 NDVI和光谱的最大似然分类结果进行对比分析。研究结果表明,纹理特征参与的分类精度较高,Kappa系数达到0.80,总分类精度为83.56%。因此,该方法更适合于半干旱区的遥感影像信息提取。
위료준학장악반간한지구적토지이용정황,이요녕성창무현위연구구,분석다시상적 HJ-1 B요감영상。의거영상각시상적귀일화식피지수(NDVI)급기파보특성,획득대영상수거적정체파악。제출기우 NDVI、광보화문리신식상결합적최대사연분류방법,통과차방법탐토해구적토지이용신식,병장분류결과여기우 NDVI화광보적최대사연분류결과진행대비분석。연구결과표명,문리특정삼여적분류정도교고,Kappa계수체도0.80,총분류정도위83.56%。인차,해방법경괄합우반간한구적요감영상신식제취。
In order to accurately know the land use situation of the semi-arid areas,this paper analyzes the multi-temporal remote sensing images with Zhangwu Country,Liaoning Province,as the research area,obtains an integral grasp of the image data based on each band characteristic and the normalized difference vegetation index (NDVI),and advances the maximum likelihood classification method with the combination of synthesized NDVI,spectral and texture information to explore the land use information and to contrast the classification results with those based on NDVI and maximum likelihood classification of spectrum.The results show that the classification accuracy of the first method is much higher,the Kappa coefficient reaching 0.80 and the total classification accuracy 83.56%.Therefore,this method is more suitable for the information extraction of remote sensing image in semi-arid areas.