精度評價步驟

1、ArcMap中進行精度評估的步驟，以東莞市為例1.根據東莞市的邊界，在區(qū)域內生成離散點，每個類設置20個樣點，供設置 200個樣本點2.提取樣本點所在圖斑的地類屬性T卯百 in the kevwtwd to find:日鄉(xiāng)Layers3 0 dg X明鵬展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.囪 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國About Joiring DataOKCancel.在生成的具有地類

6、屬性的本點中，新建一個class_num字段，字段的值通過 select byattribute分別賦值，耕地=1,園地=2,林地=3,草地=4,工礦用地=5,住宅用地=6,交 通用地=7,水域=8,基塘=9,裸地=10.在生成的具有地類屬性的樣本點中，再新建一個true_class字段，對照研究區(qū)域的spot2.5m融合圖像或者 google earth ,在true_class字段給出真正的地類編碼，具體操作如下：首先設置可用于選擇的圖層，僅 randompoint圖層可用于選擇對每個樣本點進行編輯，給出真正的地類屬性，可以通過分別對true_class字段值設 置不同的符號化顯示來區(qū)分樣

7、本點是否已經編輯過AttributesValue0- randompoint 由耕地FID3FID 1MB179CID0HD.2968C耕地cldss num ,1true.classProperty1 features5.待所有的樣本點編輯完畢，要進行分類誤差矩陣的計算參委數(shù)據分 類 數(shù) 據12345678910行總 和生產者精度用戶精度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 總 和X+1X+2X+3X+4X+5X+6X+7X+8X+9X+10N概念：i為行數(shù)，j為列數(shù)生產者精度：指某一類別的正確分類數(shù)（表中主對角線上的數(shù)據Xii）占參考數(shù)據中該類別像元總數(shù)（列數(shù)據 X+i）的比例，對應的誤差為漏分誤差。用戶精度：指某一類別的正確分類數(shù)（表中主對角線上的數(shù)據Xii）占分類數(shù)據中該類別像元總數(shù)（列數(shù)據 Xi+）的比例，對應的誤差為誤分誤差?？傮w精度：指總分類正確數(shù)（表中主對角線上的數(shù)據Xii之和）占總抽樣數(shù)數(shù) （N）的比例,它反映分類結果總的正確程度。作為分由于總體精度只利用了誤差矩陣主對角線上的元素，而未利用整個誤差矩陣的信息,類誤差的全面衡量尚欠不足，因此可以用Kappa系數(shù)指標計算公式如下：?司(Xi +?X+