GEE学习笔记|GEE学习笔记八十六（分类中的特征重要性分析） GEE学习笔记八十六：分类中的

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之前在GEE中做随机森林分类时候，很多人都在问如何做特征重要性分析？但是在GEE之前并没有相关API可以做特征重要性分析，最新的API更新后GEE也可以做特征重要性分析了。

1、目前常用的包含特征重要信息分析的分类方法包括：
（1）决策树
ee.Classifier.smileCart(maxNodes, minLeafPopulation)

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（2）随机森林
ee.Classifier.smileRandomForest(numberOfTrees, variablesPerSplit, minLeafPopulation, bagFraction, maxNodes, seed)

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这两个方法和之前的分类决策树和随机森林分类方法参数非常类似，只不过调用这两个定义方法在使用explain()方法就可以得到每个分类特征重要性的信息。

2、得到特征重要性的API方法：

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也就是之前在分类器中的方法explain，这个方法是最开始就有的，只不过是最近GEE官方加入了特征重要性的分析返回信息，下面通过一个具体例子说明一下如何使用以及具体输出。

具体代码：

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,"system:index": "0"}),ee.Feature(ee.Geometry.Point([114.31056389269042, 36.35499859187555]),{"type": 0,"system:index": "1"}),ee.Feature(ee.Geometry.Point([114.31236633714843, 36.3529248270982]),{"type": 0,"system:index": "2"}),ee.Feature(ee.Geometry.Point([114.32159313615966, 36.35461840580203]),{"type": 0,"system:index": "3"}),ee.Feature(ee.Geometry.Point([114.32348141130615, 36.35638107103549]),{"type": 0,"system:index": "4"}),ee.Feature(ee.Geometry.Point([114.32936081346679, 36.35589720612248]),{"type": 0,"system:index": "5"}),ee.Feature(ee.Geometry.Point([114.33515438493896, 36.35565527253804]),{"type": 0,"system:index": "6"}),ee.Feature(ee.Geometry.Point([114.33545479234863, 36.35855842592195]),{"type": 0,"system:index": "7"}),ee.Feature(ee.Geometry.Point([114.29468521532226, 36.35679580999553]),{"type": 0,"system:index": "8"}),ee.Feature(ee.Geometry.Point([114.29979214128662, 36.34922647614173]),{"type": 0,"system:index": "9"}),ee.Feature(ee.Geometry.Point([114.28871998247314, 36.3387180531404]),{"type": 0,"system:index": "10"}),ee.Feature(ee.Geometry.Point([114.28279766496826, 36.33833778756726]),{"type": 0,"system:index": "11"}),ee.Feature(ee.Geometry.Point([114.39553627428222, 36.3299369018726]),{"type": 0,"system:index": "12"}),ee.Feature(ee.Geometry.Point([114.39725288805175, 36.32997147527504]),{"type": 0,"system:index": "13"}),ee.Feature(ee.Geometry.Point([114.39712414201904, 36.32796619257241]),{"type": 0,"system:index": "14"}),ee.Feature(ee.Geometry.Point([114.39918407854248, 36.3291762831369]),{"type": 0,"system:index": "15"}),ee.Feature(ee.Geometry.Point([114.39845451769042, 36.3274475765775]),{"type": 0,"system:index": "16"}),ee.Feature(ee.Geometry.Point([114.40467724260498, 36.32900341420687]),{"type": 0,"system:index": "17"}),ee.Feature(ee.Geometry.Point([114.40480598863769, 36.32734385296429]),{"type": 0,"system:index": "18"}),ee.Feature(ee.Geometry.Point([114.38210377153564, 36.33612529650057]),{"type": 0,"system:index": "19"}),ee.Feature(ee.Geometry.Point([114.38034424242187, 36.336194437797644]),{"type": 0,"system:index": "20"}),ee.Feature(ee.Geometry.Point([114.38098797258544, 36.34041194086619]),{"type": 0,"system:index": "21"}),ee.Feature(ee.Geometry.Point([114.38785442766357, 36.337473540724005]),{"type": 0,"system:index": "22"}),ee.Feature(ee.Geometry.Point([114.38532242235351, 36.34227863160822]),{"type": 0,"system:index": "23"})]),nocrop =/* color: #0b4a8b *//* shown: false */ee.FeatureCollection([ee.Feature(ee.Geometry.Point([114.36832794603515, 36.344663782445146]),{"type": 1,"system:index": "0"}),ee.Feature(ee.Geometry.Point([114.37463650163818, 36.345078583829164]),{"type": 1,"system:index": "1"}),ee.Feature(ee.Geometry.Point([114.37742599901367, 36.34576991455905]),{"type": 1,"system:index": "2"}),ee.Feature(ee.Geometry.Point([114.378026813833, 36.34860430638123]),{"type": 1,"system:index": "3"}),ee.Feature(ee.Geometry.Point([114.37386402544189, 36.35009059256129]),{"type": 1,"system:index": "4"}),ee.Feature(ee.Geometry.Point([114.36987289842773, 36.352198996374725]),{"type": 1,"system:index": "5"}),ee.Feature(ee.Geometry.Point([114.37695393022705, 36.351887923993054]),{"type": 1,"system:index": "6"}),ee.Feature(ee.Geometry.Point([114.3797005122583, 36.35392715363849]),{"type": 1,"system:index": "7"}),ee.Feature(ee.Geometry.Point([114.37352070268798, 36.35627738595022]),{"type": 1,"system:index": "8"}),ee.Feature(ee.Geometry.Point([114.31785950121093, 36.34438724696162]),{"type": 1,"system:index": "9"}),ee.Feature(ee.Geometry.Point([114.31657204088378, 36.34345393245458]),{"type": 1,"system:index": "10"}),ee.Feature(ee.Geometry.Point([114.34390911516357, 36.33512274079625]),{"type": 1,"system:index": "11"}),ee.Feature(ee.Geometry.Point([114.35094723161865, 36.33723154988948]),{"type": 1,"system:index": "12"}),ee.Feature(ee.Geometry.Point([114.35309299883056, 36.337058698833104]),{"type": 1,"system:index": "13"}),ee.Feature(ee.Geometry.Point([114.35296425279785, 36.33501902740074]),{"type": 1,"system:index": "14"}),ee.Feature(ee.Geometry.Point([114.41952058729305, 36.35091929703152]),{"type": 1,"system:index": "15"}),ee.Feature(ee.Geometry.Point([114.41966006216182, 36.3516321782695]),{"type": 1,"system:index": "16"}),ee.Feature(ee.Geometry.Point([114.41982099470272, 36.35252219053154]),{"type": 1,"system:index": "17"}),ee.Feature(ee.Geometry.Point([114.42009458002224, 36.35383558809808]),{"type": 1,"system:index": "18"}),ee.Feature(ee.Geometry.Point([114.42027697023525, 36.35499343336394]),{"type": 1,"system:index": "19"}),ee.Feature(ee.Geometry.Point([114.42028233465328, 36.35609509850472]),{"type": 1,"system:index": "20"}),ee.Feature(ee.Geometry.Point([114.42228326257839, 36.35603461533285]),{"type": 1,"system:index": "21"}),ee.Feature(ee.Geometry.Point([114.42357072290554, 36.35606053669799]),{"type": 1,"system:index": "22"})]),water =/* color: #ffc82d *//* shown: false */ee.FeatureCollection([ee.Feature(ee.Geometry.Point([114.3321717685144, 36.3306024371752]),{"type": 2,"system:index": "0"}),ee.Feature(ee.Geometry.Point([114.33210739549804, 36.333592954210076]),{"type": 2,"system:index": "1"}),ee.Feature(ee.Geometry.Point([114.28854832109619, 36.41266166922327]),{"type": 2,"system:index": "2"}),ee.Feature(ee.Geometry.Point([114.29610142168212, 36.40879346635716]),{"type": 2,"system:index": "3"}),ee.Feature(ee.Geometry.Point([114.28580173906494, 36.40547771059803]),{"type": 2,"system:index": "4"}),ee.Feature(ee.Geometry.Point([114.29541477617431, 36.40160915003326]),{"type": 2,"system:index": "5"}),ee.Feature(ee.Geometry.Point([114.3057144587915, 36.40547771059803]),{"type": 2,"system:index": "6"}),ee.Feature(ee.Geometry.Point([114.29953464922119, 36.39829308768715]),{"type": 2,"system:index": "7"}),ee.Feature(ee.Geometry.Point([114.39435610231533, 36.3617846267207]),{"type": 2,"system:index": "8"}),ee.Feature(ee.Geometry.Point([114.39429172929897, 36.361179837099506]),{"type": 2,"system:index": "9"}),ee.Feature(ee.Geometry.Point([114.38845524248256, 36.35940000037548]),{"type": 2,"system:index": "10"}),ee.Feature(ee.Geometry.Point([114.38152441438808, 36.35701530096524]),{"type": 2,"system:index": "11"}),ee.Feature(ee.Geometry.Point([114.38133129533901, 36.35653143999478]),{"type": 2,"system:index": "12"}),ee.Feature(ee.Geometry.Point([114.39965614732876, 36.36371992192421]),{"type": 2,"system:index": "13"}),ee.Feature(ee.Geometry.Point([114.40693029817714, 36.3668473954689]),{"type": 2,"system:index": "14"}),ee.Feature(ee.Geometry.Point([114.41094288286342, 36.36890351124372]),{"type": 2,"system:index": "15"}),ee.Feature(ee.Geometry.Point([114.40238127168789, 36.36840244602017]),{"type": 2,"system:index": "16"})]); //监督分类Map.centerObject(roi, 11); Map.addLayer(roi, {color: "red"}, "roi", false); var sampleData = https://www.it610.com/article/crop.merge(nocrop).merge(water); //Landsat8 SR数据去云function rmCloud(image) {var cloudShadowBitMask = (1 << 3); var cloudsBitMask = (1 << 5); var qa = image.select("pixel_qa"); var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0).and(qa.bitwiseAnd(cloudsBitMask).eq(0)); return image.updateMask(mask); }//缩放function scaleImage(image) {var time_start = image.get("system:time_start"); image = image.multiply(0.0001); image = image.set("system:time_start", time_start); return image; }//NDVIfunction NDVI(image) {return image.addBands(image.normalizedDifference(["B5", "B4"]).rename("NDVI")); }//NDWIfunction NDWI(image) {return image.addBands(image.normalizedDifference(["B3", "B5"]).rename("NDWI")); }//NDBIfunction NDBI(image) {return image.addBands(image.normalizedDifference(["B6", "B5"]).rename("NDBI")); }var l8Col = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(roi).filterDate("2018-1-1", "2019-1-1").map(rmCloud).map(scaleImage).map(NDVI).map(NDWI).map(NDBI); var bands = ["B1", "B2", "B3", "B4", "B5", "B6", "B7","NDBI", "NDWI", "NDVI"]; var l8Image = l8Col.median().clip(roi).select(bands); var rgbVisParam = {min: 0,max: 0.3,bands: ["B4", "B3", "B2"]}; Map.addLayer(l8Image, rgbVisParam, "l8Image"); //生成监督分类训练使用的样本数据var training = l8Image.sampleRegions({collection: sampleData,properties: ["type"],scale: 30}); //初始化分类器// var classifier = ee.Classifier.smileCart().train({//features: training,//classProperty: "type",//inputProperties: bands// }); var classifier = ee.Classifier.smileRandomForest(10).train({features: training,classProperty: "type",inputProperties: bands}); print(classifier.explain()); //影像数据调用classify利用训练数据训练得到分类结果var classified = l8Image.classify(classifier); //训练结果的混淆矩阵var trainAccuracy = classifier.confusionMatrix(); Map.addLayer(classified, {min:0, max:2, palette: ["green", "orange", "blue"]}, "classify");

代码分析：
（1）这段代码做的是一个简单的监督分类，通过自己标注的样本将影像分成3大类（作物、非作物和水），样本由于是我做测试随机标注的，所以测试精度和分类准确度不高。
（2）和之前的分类代码相比较，这里只是更换了分类方法，比如随机森林我使用的是smileRandomForest方法，参数和之前的方法的参数类似，这里只是简单设置了树的个数为10。
（3）然后对分类器调用explain方法就可以查看特征重要性信息，输出结果如下面所示。

运行结果：
比如使用ee.Classifier.smileRandomForest()方法做的分类结果，使用explain后的信息如下图：

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大家如果有问题需要交流或者有项目需要合作，可以微信联系我，加微信好友请留言加上“GEE”。
知乎专栏：https://zhuanlan.zhihu.com/c_123993183
CSDN：https://blog.csdn.net/shi_weihappy
微信号：shi_weihappy
【GEE学习笔记|GEE学习笔记八十六（分类中的特征重要性分析）】