Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images

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