confusionMatrix
# 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 |
# 示例
image = pie.Image('user/17090142114/PGDB001/World84').select(["B1","B2","B3"])
featureCollection = pie.FeatureCollection('user/17090142114/PGDB001/WorldROI')
training = image.sampleRegions(featureCollection,["type"],50000).getInfo()
classifer = pie.RT().train(training,"type",["B1","B2","B3"])
resultImage = image.classify(classifer,"classifyA")
result = classifer.confusionMatrix().getInfo()
matrix = pie.ConfusionMatrix(result.get('type'),result.get('array'))
kappa = matrix.kappa()
print(kappa.getInfo())
# 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 |
# 示例
image = pie.Image('user/17090142114/PGDB001/World84').select(["B1","B2","B3"])
featureCollection = pie.FeatureCollection('user/17090142114/PGDB001/WorldROI')
training = image.sampleRegions(featureCollection,["type"],50000).getInfo()
classifer = pie.RT().train(training,"type",["B1","B2","B3"])
resultImage = image.classify(classifer,"classifyA")
result = classifer.confusionMatrix().getInfo()
matrix = pie.ConfusionMatrix(result.get('type'),result.get('array'))
acc = matrix.acc()
print('acc=',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 |
# 示例
image = pie.Image('user/17090142114/PGDB001/World84').select(["B1","B2","B3"])
featureCollection = pie.FeatureCollection('user/17090142114/PGDB001/WorldROI')
training = image.sampleRegions(featureCollection,["type"],50000).getInfo()
classifer = pie.RT().train(training,"type",["B1","B2","B3"])
resultImage = image.classify(classifer,"classifyA")
result = classifer.confusionMatrix().getInfo()
matrix = pie.ConfusionMatrix(result.get('type'),result.get('array'))
kappa = matrix.kappa()
print('kappa=', kappa.getInfo())
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上次更新: 2022/05/25, 07:37:39