实验六 遥感影像分类.docVIP

实验 多光谱遥感影像分类 [实验目的]：掌握常规的多光谱遥感影像分类操作及其处理。比较非监督分类法和监督分类法的结果，二者之间的优缺点。这些分类处理包括聚合处理（clump）、筛选处理（sieve）、并类处理（combine），以及精度评估。 [实验内容]：多光谱影像分类的几种方法，并比较之间的区别； 图像分类的后期处理； [实验准备]：数据：文件夹 envidata/can_tm File Description can_tmr.img Boulder Colorado TM Reflectance can_tmr.hdr ENVI Header for Above can_km.img K MEANS Classification can_km.hdr ENVI Header for Above can_iso.img ISODATA Classification can_iso.hdr ENVI Header for Above classes.roi Regions of Interest (ROI) for Supervised Classification can_pcls.img Parallelepiped Classification can_pcls.hdr ENVI Header for Above can_bin.img Binary Encoding Result can_bin.hdr ENVI Header for Above can_sam.img SAM Classification Result can_sam.hdr ENVI Header for Above can_rul.img Rule image for SAM classification can_rul.hdr ENVI Header for Above can_sv.img Sieved Image can_sv.hdr ENVI Header for Above can_clmp.img Clump of sieved image can_clmp.hdr ENVI Header for Above can_comb.img Combined Classes image can_comb.hdr ENVI Header for Above can_ovr.img Classes overlain on gray scale image can_ovr.hdr ENVI Header for Above can_v1.evf Vector layer generated from class #1 can_v2.evf Vector layer generated from class #2 [实验步骤]： 1.打开并显示can_tmr（Landsat TM）彩色432，影像特征分析 Use the displayed color image as a guide to classification. This image is the equivalent of a false color infrared photograph. Even in a simple three-band image, it’s easy to see that there are areas that have similar spectral characteristics. Bright red areas on the image represent high infrared reflectance, usually corresponding to healthy vegetation, either under cultivation, or along rivers. Slightly darker red areas typically represent native vegetation, in this case in slightly more rugged terrain, primarily corresponding to coniferous trees. Several distinct geologic and urbanization classes are also readily apparent as is urbanization. 2.光标位置/值 Select Tools → Cursor Location/Value from the Main Image window menu bar. Alternatively, doub