reference-cover

Earlier we have written about assessing classification accuracy. The post dealt with creating confusion matrix. But the topic is not resolved with that as it has some more interesting points. While assessing the accuracy of automated classification results one has to answer these two questions: which assessment method should be selected? where and how to […]

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minimum-distance

We have already posted a material about supervised classification algorithms, it was dedicated to parallelepiped algorithm. Now we are going to look at another popular one – minimum distance. In contrast with the parallelepiped classification, it is used when the class brightness values overlap in the spectral feature space (more details about choosing the right […]

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supervised-classification

Image classification is a means of satellite imagery decryption, that is, identification and delineation of any objects on the imagery. Classification is an automated methods of decryption. The user does not need to digitize the objects manually, the software does is for them. According to the degree of user involvement, the classification algorithms are divided […]

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confusion-matrix

Applying any classification algorithm to interpret a remotely sensed image we are always interested in the result accuracy. The simplest way to assess it is the visual evaluation. Comparing the image with the results of its interpretation, we can see errors and roughly estimate their size. But if we need a reliable accuracy assessment, we […]

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