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Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images

Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images

Sucithra B., Angelin Gladston
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 24
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.298648
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MLA

Sucithra B., and Angelin Gladston. "Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images." IJIRR vol.12, no.1 2022: pp.1-24. http://doi.org/10.4018/IJIRR.298648

APA

Sucithra B. & Gladston, A. (2022). Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-24. http://doi.org/10.4018/IJIRR.298648

Chicago

Sucithra B., and Angelin Gladston. "Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-24. http://doi.org/10.4018/IJIRR.298648

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Abstract

Deep learning is a powerful state-of-the-art technique for image processing, including remote sensing images. This paper describes a multilevel deep learning based crop type identification system that targets land cover and crop type classification from multi-temporal multisource satellite imagery. The proposed crop type identification is based on unsupervised neural network that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised neural networks. The main part of this multilayer deep network with Self Organizing maps and atmospheric correction is an ensemble of CNNs. The proposed system is applied for crop identification using Landsat-8 time-series and implemented with different sized vector data, parcel boundary. Aided with self-organizing maps and atmospheric correction for pre-processing doing both pixel based and parcel based analysis, this proposed crop type identification system allowed us to achieve the overall classification accuracy of nearly 95% for three different time periods.