精度评价步骤

1、ArcMap中进行精度评估的步骤，以东莞市为例1.根据东莞市的边界，在区域内生成离散点，每个类设置20个样点，供设置 200个样本点2.提取样本点所在图斑的地类属性T卯百 in the kevwtwd to find:日乡Layers3 0 dg X明鹏展3 dql司 Open Attribute Table口 oann To Layer.依 Zoom To IVIke VisrbleJoin.,Rfirriove JoinRelate!. Convert Features to Graphics. Convert Sy rn fee logy to Fepresentatiori.Fie m

2、ove Rekte(s) Save As L自pr File.囱 Properties|De-Sac Masks fcirtographulJoin DataJoin late you append additional data to this layers attribute tsbl& so you can, for examlej symbolize the layers features using this data.What do you want to join to ths layer?| Join data frcm another la/er based on spati

3、al locationChoose the layer 3 join 8 this laryefj or had spatial data frcm disk;dglucc| GfirVou are jainrig: PWgons to FaintsSalact a join Feature da55 ahova. You 科il be given diFFarent ptionf based on qecmetry types cf the source Mature 由笈 and the join fsaturs class,Each point 冉 III bt given all th

4、e a tributes of the polygon that;* t Fais hsbde.F a point Mis inside more than one poh/on (for example, because the layer being jciined contains overlapping polygons) the attributes oF the first polygon Found 囚ill be joined.C 后 closest to it.A distant白 Field dded shwjing h口*1 close the polyigan is (

5、in the tri但 of the target layer), A poison that the point 包I仁 inside I treated os beingwest bo the point。怠 0 distance oFO).Tlie result of the join iill be saved into a new ley er.Specif output shiapeNle or feature da弱 for t做 new layer:| E :dortggLjarirandampnt.shp国About Joiring DataOKCancel.在生成的具有地类

6、属性的本点中，新建一个class_num字段，字段的值通过 select byattribute分别赋值，耕地=1,园地=2,林地=3,草地=4,工矿用地=5,住宅用地=6,交 通用地=7,水域=8,基塘=9,裸地=10.在生成的具有地类属性的样本点中，再新建一个true_class字段，对照研究区域的spot2.5m融合图像或者 google earth ,在true_class字段给出真正的地类编码，具体操作如下：首先设置可用于选择的图层，仅 randompoint图层可用于选择对每个样本点进行编辑，给出真正的地类属性，可以通过分别对true_class字段值设 置不同的符号化显示来区分样

7、本点是否已经编辑过AttributesValue0- randompoint 由耕地FID3FID 1MB179CID0HD.2968C耕地cldss num ,1true.classProperty1 features5.待所有的样本点编辑完毕，要进行分类误差矩阵的计算参委数据分 类 数 据12345678910行总 和生产者精度用户精度1X11X12X110X1 +(X11/X+1)*100%(X11/X1+)*100%2X21X22X210X2+(X22/X+2)*100%(X22/X2+)*100%3X31X32X33X310X3+(X33/X+3)*100%(X33

8、/X3+)*100%4X41X44X410X4+(X44/X+4)*100%(X44/X4+)*100%5X51X55X510X5+(X55/X+5)*100%(X55/X5+)*100%6X61X66X610X6+(X66/X+6)*100%(X66/X6+)*100%7X71X77X710X7+(X77/X+7)*100%(X77/X7+)*100%8X81X88X810X8+(X88/X+8)*100%(X88/X8+)*100%9X91X99X910X9+(X99/X+9)*100%(X99/X9+)*100%10X101X1010X10+(X1010/X+10)*100%(X1010

9、/X10+)*100%列 总 和X+1X+2X+3X+4X+5X+6X+7X+8X+9X+10N概念：i为行数，j为列数生产者精度：指某一类别的正确分类数（表中主对角线上的数据Xii）占参考数据中该类别像元总数（列数据 X+i）的比例，对应的误差为漏分误差。用户精度：指某一类别的正确分类数（表中主对角线上的数据Xii）占分类数据中该类别像元总数（列数据 Xi+）的比例，对应的误差为误分误差。总体精度：指总分类正确数（表中主对角线上的数据Xii之和）占总抽样数数 （N）的比例,它反映分类结果总的正确程度。作为分由于总体精度只利用了误差矩阵主对角线上的元素，而未利用整个误差矩阵的信息,类误差的全面衡量尚欠不足，因此可以用Kappa系数指标计算公式如下：?司(Xi +?X+