parallelepiped-classification

The previous post was dedicated to picking the right supervised classification method. And this time we will look at how to perform supervised classification in ENVI. We will take parallelepiped classification as an example as it is mathematically the easiest algorithm. In ENVI working with any other type of supervised classification is very similar to […]

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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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scatter-plot-

There are different ways to illustrate objects’ spectral brightness variation when moving between different spectral ranges. If it has to be shown for a single pixel of an image or generalized for a group of pixels, then spectral brightness curves are used. And what has to be done when one wishes to display spectral characteristics […]

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ROI-separability-envi

Modern software for satellite image processing offers its users a wide range of supervised classification algorithms (more detail can be found here). It yields powerful capabilities for automation of the image interpretation process. In return for that, a user should make training areas of high quality. It is this quality what defines the accuracy of the […]

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