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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 s...

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating maps, traffic management, etc. In this paper, new band combination (B652) to form road index (RI) OLI multispectral bands based on spectral reflectance asphalt, presented for extraction. The B652 converted by normalization. morphological operat...

Nearshore coastal waters are highly dynamic in both space and time. They can be difficult to sample using conventional methods due their shallow depth, tidal variability, the presence of strong currents breaking waves. High resolution satellite sensors used provide synoptic views Surface Temperature (ST), but performance such ST products nearshore zone is poorly understood. Close shoreline, pix...