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Design of Deep Convolution Neural Networks for categorical signature classification of raw panchromatic satellite images

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Abstract

Remote Sensing categorical signature classification has gained significant implications on spatial resolution image analysis due to differences in the sensors’ spatial response and surface variations. As a consequence, the grey level co-occurrence of probability texture features for the classification task is crucial. Traditionally, deep learning-based Convolution Neural Network (CNN) classifiers for spectral/spatially scaled signatures (Hyperspectral or Multispectral images) extract deep features and accurately classify remote sensing scenes into appropriate labels/categories. While dealing with raw panchromatic images, the spatial with varied angular signatures will have untrained grey scale patterns, translational and rotational variations. It is still a bottleneck to label and classify data using pre-trained models from two separate sources based on its spatial structural characteristics. In this paper, a thirteen-layer deep CNN model is designed for categorical signature classification of the raw panchromatic satellite dataset. The design is carried out in three stages- Firstly, the method extracts the global content and meanings of remote sensing images at the scene level. Then, it cross compares with training and testing of identified complex remote sensing signatures in raw inter dataset images with large inter and intra-class variations. Finally, the validation of the 70:30 training-testing set is done to classify a batch of images into the respective labeled signatures (Land and sea) with an accuracy of 88.9%. The modified versions of five state-of-the-art pre-trained classifiers are tested to check the efficacy of the proposed approach.

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Availability of data and material

The datasets procured from National Remote Sensing Center (NRSC), Indian Space Research Organization (ISRO), India, for educational purposes. The authors would like to thank the NRSC organization for providing the datasets. Since the data is not available for commercial use, the images used for research purposes are shown in the manuscript.

Code availability

The software tool is readily available in MATLAB R19 B and higher versions. The design is done, as mentioned in Section-2. There is no code for this paper.

Notes

  1. https://www.mathworks.com/help/deeplearning/ref/alexnet.html

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Acknowledgments

We thank National Remote Sensing Center, Indian Space Research Organisation, Hyderabad, India, for providing the IRS P5 Image data set for educational purposes. We also thank the National Institute of Technology Puducherry, Karaikal, India, for providing research facilities in this area.

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The authors received no grant.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology, Puducherry, India

    G. Rohith & Lakshmi Sutha Kumar

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G. Rohith declares that he has no conflict of interest. Lakshmi Sutha Kumar declares that she has no conflict of interest.

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Rohith, G., Kumar, L.S. Design of Deep Convolution Neural Networks for categorical signature classification of raw panchromatic satellite images. Multimed Tools Appl 81, 28367–28404 (2022). https://doi.org/10.1007/s11042-022-12928-7

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