Shahrul

Hi All, Hopefully this group can give me answer on my Question. First round, I use EnMAP Box 3 which is plugin in QGIS. I use it for image classification which the result is classified image into 7 class in BSQ format. The algorithm I use for classification is Random Forest, Gaussian and LinearSVC. EnMap use my Training ROI to proses in Random Forest, Gaussian and LinearSVC classifier/algorithm. But on accuracy assessment EnMAP (Random Forest, Gaussian and LinearSVC) did not use testing ROI to validated the classified image. Because the classified image is in BSQ format, I can transfer it to Envi software (hope you know that). And test it with my Testing ROI using it tools Confusion Matrix Using Ground Truth ROIs . And I get the statistic for Overall Accuracy and Kappa value of the classified Images. Second round I use Envi tools/ algorithm like SVM and SAM for image classification. After get the result which is classified image into 7 class (Building, grass, path etc) . I use Envi tools Confusion Matrix Using Ground Truth ROIs . And I get the statistic for Overall Accuracy and Kappa value of the classified Images. Third round From the statistic create using Confusion Matrix Using Ground Truth ROIs . I compare the performance of classified images between SVM, SAM, Random Forest, Gaussian and LinearSVC by comparing the Overall Accuracy and Kappa value. All classifier like SVM, SAM, Random Forest, Gaussian and LinearSVC using same images before classify, same ROI for training and testing and same tools to validate which is Envi tools Confusion Matrix Using Ground Truth ROIs Final round After present my friend said something like It is not correct to compare SVC, SAM, Random Forest, Gaussian and LinearSVC. This is because SAM and SAM classified the image in Envi Environment. While Random Forest, Gaussian and LinearSVC classified the image in EnMAP @ QGIS Environment. Even using same validation tools which is Envi tools Confusion Matrix Using Ground Truth ROIs. Is it true?