An object classification technique on aerial and space imagery under low separability of recognition features
Keywords:aerial and space images, unsupervised classification, supervised classification, clustering, training sample, training sample separability
Classification methods are used in remote sensing of the Earth to study natural resources, monitor the environment, and solve many other problems. Also, they play an important role in involving the remotely sensed data in solving tasks related to achieving sustainable development goals. Among them are supervised and unsupervised classification methods on aerial and space imagery. However, for most thematic problems, it is advisable to use precisely supervised classification methods because the considered problems require setting the characteristics of the initial classes. In supervised classification methods, the features are given by the training sample. Among the well-known approaches to processing the training sample, the following can be distinguished: cluster sampling; approaches to reduce the size of the training sample; approaches that detect representatives who were assigned to the sample of the wrong class to which their class membership corresponds. However, their common disadvantages is that they do not consider the factor of separation of the training sample. This property directly affects the reliability of the classification. The research proposed a technique to increase the reliability of object classification on aerial and space imagery by increasing the separability of the training sample. This technique includes a method of assessing the separability of the training sample. At the same time, it is possible to assess the separability of both two separate classes and the entire set of the educational sample. The developed technique has two branches of application: reducing the size of the training sample and clustering the training sample. In the study, the effectiveness of this technique was experimentally tested on three examples. In two examples, the technique was used to cluster the training sample. In one of these examples, the overall accuracy of the classification increased by 4% (from 77% to 81%), and in the second one by 20% (from 63% to 83%). A reduction in the size of the training sample was applied to the third example. As a result, the dimensionality of the input data was reduced from 167 to 57 layers. That is, the dimensionality decreased by 2.92 times. Also, the overall accuracy of the classification was increased by 2% (from 91% to 93%).
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