DETECTION OF CLOUD SHADOWS USING DEEP CNN UTILISING SPATIAL AND SPECTRAL FEATURES OF LANDSAT IMAGERY

Authors

  • M.S. Antony Vigil Department of Computer Science and Engineering. SRM Institute of Science and Technology, Chennai, India
  • Aashna Chib Department of Computer Science and Engineering. SRM Institute of Science and Technology, Chennai, India
  • Ayushi Vashisth Department of Computer Science and Engineering. SRM Institute of Science and Technology, Chennai, India
  • Tanisha Pattnaik Department of Computer Science and Engineering. SRM Institute of Science and Technology, Chennai, India

DOI:

https://doi.org/10.58973/CTRJ.221122

Keywords:

Convolutional Neural Network (CNN), Cloud Detection, Semantic Segmentation, Satellite Imager, Landsat, Fuzzy-C

Abstract

Aim: The proposed work emphasizes here on detection of cloud shadows using Deep CNN (Convolutional Neural Networks) utilizing spatial and spectral features of Landsat imagery.

Results: In the current study deep CNN Algorithm is used for cloud and its shadow detection. We used python libraries to create a CNN. Fourier transformation is applied on that array to transform as per their requirements. Conclusion: Using the Deep CNN algorithm, we were able to combine the whole input image to get multilevel features. Deep CNN does better image processing and semantic segmentation when compared with existing fuzzy-c and f-masking.

HIGHLIGHTS:

  1. An improved approach using Deep CNN (Convolutional Neural Network) does better image processing and semantic segmentation when compared with existing fuzzy-c and f-masking.

 

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Published

2022-10-05

How to Cite

M.S. , A. V., Chib, A., Vashisth, A., & Pattnaik, T. (2022). DETECTION OF CLOUD SHADOWS USING DEEP CNN UTILISING SPATIAL AND SPECTRAL FEATURES OF LANDSAT IMAGERY. Computing Technology Research Journal, 1(1), 22–29. https://doi.org/10.58973/CTRJ.221122

Issue

Vol. 1 No. 1 (2022): October: Computing Technology Research Journal

Section

Short Communication

License

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