简介

提供了混淆矩阵类，可以实现对影像分类结果进行精度评价。

confusionMatrix

构造函数，返回一个由给定参数构建的混合矩阵对象。

函数	返回值
confusionMatrix(type=null, array=null)	ConfusionMatrix

参数	类型	说明
type	Array	混合矩阵中类型的数组对象，默认为null
array	Array	混合矩阵的数组对象，默认为null

var image = pie.Image('user/17090142114/PGDB001/World84').select(["B1","B2","B3"]);
var featureCollection = pie.FeatureCollection('user/17090142114/PGDB001/WorldROI');
var training = image.sampleRegions(featureCollection,["type"],50000);
var classifier = pie.Classifier.rTrees().train(training,"type",["B1","B2","B3"]);
var resultImage = image.classify(classifier,"classifyA");
var visParam = {
  opacity:1,
  uniqueValue:'1,2,3,4',
  palette: 'EAF2F5,000032,1F3600,FAFFC8'
};

Map.addLayer(resultImage,visParam);
Map.setCenter(0,0,0);

var matrix = classifier.confusionMatrix();
var acc = matrix.acc();
print(acc);

acc

返回混合矩阵的精确度。

函数	返回值
acc()	混合矩阵的精确度计算结果

var image = pie.Image('user/17090142114/PGDB001/World84').select(["B1","B2","B3"]);
var featureCollection = pie.FeatureCollection('user/17090142114/PGDB001/WorldROI');
var training = image.sampleRegions(featureCollection,["type"],50000);
var classifier = pie.Classifier.rTrees().train(training,"type",["B1","B2","B3"]);
var resultImage = image.classify(classifier,"classifyA");
var visParam = {
  opacity:1,
  uniqueValue:'1,2,3,4',
  palette: 'EAF2F5,000032,1F3600,FAFFC8'
};

Map.addLayer(resultImage,visParam);
Map.setCenter(0,0,0);

var matrix = classifier.confusionMatrix();
var acc = matrix.acc();
print(acc.getInfo());

kappa

返回混合矩阵的精确度。

函数	返回值
kappa()	混合矩阵的kappa系数计算结果

var image = pie.Image('user/17090142114/PGDB001/World84').select(["B1","B2","B3"]);
var featureCollection = pie.FeatureCollection('user/17090142114/PGDB001/WorldROI');
var training = image.sampleRegions(featureCollection,["type"],50000);
var classifier = pie.Classifier.rTrees().train(training,"type",["B1","B2","B3"]);
var resultImage = image.classify(classifier,"classifyA");
var visParam = {
  opacity:1,
  uniqueValue:'1,2,3,4',
  palette: 'EAF2F5,000032,1F3600,FAFFC8'
};

Map.addLayer(resultImage,visParam);
Map.setCenter(0,0,0);

var matrix = classifier.confusionMatrix();
var kappa = matrix.kappa();
print(kappa);