Abstract
This paper focuses on the land cover/usage area classification problem by using local averaging for feature extraction method. In hyperspectral image classification tasks, spatial information is also useful as much as spectral information. A pipeline of methods is utilized using Fisher's discriminant analysis for dimension reduction, z-score value for central limiting and support vector machines and extreme learning machines for classification. The classification accuracies on transformed data set are outperforming previous works by achieving %99.51 success ratio on for support vector machines and %99.73 for extreme learning machines on 10-fold cross validation. the proposed method increases classification accuracy significantly while reducing the dimension of the original data by %95.
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Subject Areas
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).