根据选择的样本点,并通过添加不同的指数("NDBI", "NDWI", "NDVI","B1", "B2", "B3", "B4", "B5", "B6", "B7")来增加识别度,然后求取最后的分类精度,最后通过cart分类和随机森林分类对比验证精度结果,可以分别求出两者的混淆矩阵和精度。
代码:
var roi =
/* color: #d63000 */
/* shown: false */
/* displayProperties: [
{
"type": "rectangle"
}
] */
ee.Geometry.Polygon(
[[[114.23790821489501, 36.43657462800738],
[114.23790821489501, 36.29834769127675],
[114.49265369829345, 36.29834769127675],
[114.49265369829345, 36.43657462800738]]], null, false),
crop =
/* color: #98ff00 */
/* shown: false */
ee.FeatureCollection(
[ee.Feature(
ee.Geometry.Point([114.31343922075439, 36.356156419842804]),
{
"type": 0,