我正在做监督分类，我得到的错误是’超过了用户内存限制’，但是我的数据已经不是那么复杂了。我把我的inputPropeties导出为一个堆叠的图层，我在一个光栅图层上做分类，我选择了有限的类的数量，但仍然得到这个错误。 我希望得到你的回答。 当我们出现下面的状况应该怎们办？ Resubstitution error matrix: ConfusionMatrix (Error) User memory limit exceeded. Training overall accuracy: Number (Error) User memory limit exceeded. Training kappa accuracy: Number (Error) User memory limit exceeded. Training producer accuracy: Array (Error) User memory limit exceeded. Training Consumer accuracy: Array (Error) User memory limit exceeded. 这里我们可以选择一种方案就是降低分辨率： stratifiedSample(numPoints, classBand, region, scale, projection, seed, classValues, classPoints, dropNulls, tileScale, geometries) Extracts a stratified random sample of points from an image. Extracts the specified number of samples for each distinct value discovered within the ‘classBand’. Returns a FeatureCollection of 1 Feature per extracted point, with each feature having 1 property per band in the input image. If there are less than the specified number of samples available for a given class value, then all of the points for that class will be included. Requires that the classBand contain integer values. 从图像中提取分层随机样品。 提取“ classband”中发现的每个不同值的指定数量的样本。 返回每个提取点1个特征的特征汇编，每个功能在输入图像中每个频段具有1个属性。 如果给定类值的示例数量少于指定数量的示例，则将包括该类的所有要点。 要求class带包含整数值。 Arguments: this:image (Image): The image to sample.

numPoints (Integer): The default number of points to sample in each class. Can be overridden for specific classes using the ‘classValues’ and ‘classPoints’ properties.

classBand (String, default: null): The name of the band containing the classes to use for stratification. If unspecified, the first band of the input image is used.

region (Geometry, default: null): The region to sample from. If unspecified, the input image’s whole footprint is used.

scale (Float, default: null): A nominal scale in meters of the projection to sample in. Defaults to the scale of the first band of the input image. 修改这个参数：将30转化为100或者更多来调试 projection (Projection, default: null): The projection in which to sample. If unspecified, the projection of the input image’s first band is used. If specified in addition to scale, rescaled to the specified scale.

seed (Integer, default: 0): A randomization seed to use for subsampling.

classValues (List, default: null): A list of class values for which to override the numPoints parameter. Must be the same size as classPoints or null.

classPoints (List, default: null): A list of the per-class maximum number of pixels to sample for each class in the classValues list. Must be the same size as classValues or null.

dropNulls (Boolean, default: true): Skip pixels in which any band is masked.

tileScale (Float, default: 1): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.

geometries (Boolean, default: false): If true, the results will include a geometry per sampled pixel. Otherwise, geometries will be omitted (saving memory).

Returns: FeatureCollection

var EG_LC = ee.FeatureCollection("projects/ee-samartarek440/assets/EG_LC"),

ROI = ee.FeatureCollection("projects/ee-samartarek440/assets/Agri2020"),

EG_LC_tif = ee.Image("projects/ee-samartarek440/assets/EG_LC_tif"),

Rectangle =

/* color: #98ff00 */

/* shown: false */

/* locked: true */

/* displayProperties: [

{

"type": "rectangle"

}

] */

ee.Geometry.Polygon(

[[[31

参考文章

评论可见，请评论后查看内容，谢谢！！！
 您阅读本篇文章共花了：