Lab6 non-parametric classification and post classification
12021005龚鑫烨Objection：the major object of the current lab section are to implement non-parametric classification based on BP networks and support vector machines algorithms,with a full mastery of post-classification operation. Data: the subset of spot 5 imagery covering NJ.
Steps:
1、identify a training dataset and an independent set of validation data for built-up, forest,cropland,grassland and water.
2、Implementing above-mentioned non-parametric algorithms to classify your image.
3、Validating your classification.
4、Refining your classification by implementing the majority filtering and modeling process if possible.
实验步骤：
1、将数据加载到envi中
building、water、grass）
保存ROI
3、BP分类。

Classification——supervised——neural net，设置参数及输出路径
观察RMS动态
加载变换后的图像，和原图像进行对比
Classification——post classification——confusion matrix——using ground truth ROIs
由上图可以看出精确度为99.8%
Bp分类的校正
Classification——post classification——majority analysts ，进行网格设置
通过对这两个图层地理连接，查看校正的效果
4、svm分类方法：classification——supervised——support vector machine
Svm分类效果的验证
Classification——post classification——confusion matrix——using ground truth ROIs
Svm分类的校正
Classification——post classification——majority analysts ，进行网格设置
将生成的图像与svm图像进行地理连接，查看校正效果
Basic tools——sunset data via ROIs
5、erdas里建模修改误分的像元
以support vector machine分类的图像为例
将切好的图像和之前的svm图像加载进来，并修改他们的投影信息为基于WGS84的UTM 投影。

Molder-----molder maker，建立模型
建立关系式
运行模型后加载图像
（模型一运行就闪退。

）最后进行GEO link地理连接。