Abstract

The greatest challenge posed by hyperspectral image (HSI) classification is extracting features which are useful for classification. Convolutional autoencoders (CAEs) are designed to generate relevant and generalized features from this kind of data, and advancements such as stacked CAEs (SCAEs) and multiloss CAEs (MCAEs) attempt to improve upon the model. In this experiment, we train these models on randomly selected labeled samples of the Pavia University HSI dataset. A support vector machine (SVM) uses the features extracted by these models to make predictions about unlabeled data. The accuracy of these predictions are used to evaluate the quality of each feature extractor.

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