通过设置class_doit 的参数 std_mult=3.0 即可。 完整代码如下： 参考引用：http://hi.baidu.com/dyqwrp/item/c8318cea9cec732c5a7cfbab pro Parallelpiped_method COMPILE_OPT idl2 ;恢复ENVI_SAVE文件 ENVI,/restore_base_save_files ;开始批处理模式 ENVI_BATCH_INIT,log_file='batch.log' ;编写批处理过程 ;选择文件 inputfile= 'E:\海二相关\掩膜\jpproject.img' CATCH, Error_status errorshow = 'Sorry to see the error,'+ $ ' please send the error Information to "myyouthlife@163.com"' IF Error_status NE 0 THEN BEGIN tmp = DIALOG_MESSAGE(errorshow+STRING(13b)+$ !ERROR_STATE.MSG,/error,title = '错误提示!') return ENDIF ;输入数据预处理 ENVI_OPEN_FILE, inputfile, r_fid=fid IF (fid EQ -1) THEN BEGIN RETURN ENDIF ;获取输入文件信息 ENVI_FILE_QUERY, fid, dims=dims, nb=nb pos = LINDGEN(nb) ; 输出分类图像名称 outputfile='d:\result.img' out_name = outputfile ;-平行六面体分类 0 ENVI_RESTORE_ROIS, 'E:\海二相关\掩膜\11.roi' ; 获取感兴趣区文件信息 roi_ids = ENVI_GET_ROI_IDS(fid=fid, $ roi_colors=roi_colors, roi_names=class_names) class_names = ['Unclassified', class_names] num_classes = N_ELEMENTS(roi_ids) ; Set the unclassified class to black and use roi colors lookup = BYTARR(3,num_classes+1) lookup[0,1] = roi_colors ; 计算类ROI的基本统计信息 mean = FLTARR(N_ELEMENTS(pos), num_classes) stdv = FLTARR(N_ELEMENTS(pos), num_classes) cov = FLTARR(N_ELEMENTS(pos),N_ELEMENTS(pos),num_classes) FOR j=0, num_classes-1 DO BEGIN ; roi_dims=[ENVI_GET_ROI_DIMS_PTR(roi_ids[j]),0,0,0,0] ENVI_DOIT, 'envi_stats_doit', fid=fid, pos=pos, $ dims=roi_dims, comp_flag=4, mean=c_mean, $ stdv=c_stdv, cov=c_cov MEAN[0,j] = c_mean stdv[0,j] = c_stdv cov[0,0,j] = c_cov ENDFOR ;设定阈值 thresh=REPLICATE(0.1,num_classes) out_bname = 'parallelepiped' ; 分类 ENVI_DOIT, 'class_doit', fid=fid, pos=pos, dims=dims, $ out_bname=out_bname, out_name=out_name, method=0, $ mean=mean, stdv=stdv, std_mult=3.0, $ lookup=lookup, class_names=class_names, $ r_fid=r_fid,in_memory=0 ; ; 退出批处理模式 ;envi_batch_exit end