Multi-view learning for hyperspectral image classification: An overview

TL;DR: Experimental assessment on two HSIs confirms that the proposed WDRKNN structure can significantly enhance HSI classification as compared to related state-of-the-art methodologies.

TL;DR: In this article , two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters.

TL;DR: A multistrategy evolutionary multiobjective method based on roulette wheel selection and the genetic algorithm (RWS-GA) for endmember extraction and an offspring complement mechanism (OCM) is presented to prevent duplicate individuals from appearing in historical archives.

TL;DR: Wang et al. as mentioned in this paper proposed a spectral-spatial siamese neural network with bag-of-features (S3BoF) for hyperspectral image (HSI) classification.

TL;DR: Transformer networks as mentioned in this paper enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM).

TL;DR: This framework of composite kernels demonstrates enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only, flexibility to balance between the spatial and spectral information in the classifier, and computational efficiency.

TL;DR: This overview reviews theoretical underpinnings of multi-view learning and attempts to identify promising venues and point out some specific challenges which can hopefully promote further research in this rapidly developing field.