Perspectives of deep learning based satellite imagery analysis and efficient training of the U-Net architecture for land-use classification

A huge amount of high resolution satellite images are used in different fields such as environmental observation, climate forecasting, urban planning, public services, and precision agriculture. Especially, remote sensing techniques are important for land-use monitoring which is the most important task regarding the effective management of agricultural activities. And traditional object detection and classification algorithms are inaccurate, time wasting and unreliable to solve the problem and also many researchers discuss and introduce the current domain but still results are not good enough. Hence, this paper focuses on deep learning based approaches for object detection and classification of satellite images. This paper is devoted to implementation and effective training of U-Net fully convolutional neural network architecture for semantic segmentation of satellite imagery.