Hyperspectral Image Classification by Using K-Nearest Neighbor Algorithm
DOI:
https://doi.org/10.61841/jz3nvx34Keywords:
K-Nearest Neighbour (KNN), Hybrid Features, Residual Learning, Feature FusionAbstract
Recently, deep learning has been acknowledged as one of the strong tools for feature extraction to effectively address nonlinear problems and is employed in image processing tasks and has attained good performance. However, excessively increasing the depth of the network will lead to overfitting and gradient vanishing. To address these issues, a deep feature fusion network (DFFN) is introduced. With the application of the k-nearest neighbor (KNN) algorithm in combination with residual learning, the classification accuracy is improved by extracting much more discriminative features of HSI and also easing the training of deep networks. It also extracts the hybrid features of the various classes in the image. The proposed model combines the results of various hierarchical layers that improve the classification accuracy.
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