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Multi-Scale Dilated Residual Convolutional Neural Network for Hyperspectral Image Classification
K. Pooja, R.R. Nidamanuri,
Published in IEEE Computer Society
Volume: 2019-September
Recently, deep Convolutional Neural Networks (CNNs) have been extensively studied for hyperspectral image classification. It has undergone significant improvement as compared to conventional classification methods. Yet, there are not much studies have been taken on sub-sampled ground truth dataset in CNN. This paper exploits CNN-based method along with multi-scale and dilated convolution with residual connection concepts for hyperspectral image classification on exclusive real time data set. Two raw and one standard full ground truth Pavia University datasets are used to characterize the performance. Out of raw exclusive datasets, one was taken over urban areas of Ahmedabad, India under ISRO-NASA joint initiative for HYperSpectral Imaging (HYSI) programme, and the other was collected using Hypersec VNIR integrated camera of our institute surroundings from the rooftop of the building. © 2019 IEEE.
About the journal
JournalData powered by TypesetWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
PublisherData powered by TypesetIEEE Computer Society