Analyzing satellite images by apply deep learning instance segmentation of agricultural fields

Abstract

This novel research focuses on multi-exposure satellite images of agricultural fields using image analysis and deep learning techniques. The development of image edge smoothening system using CNN is in hot pursuit, with special attention being given to the smoothening of all the edges of image. Given its high propensity to meta-size, going hand in hand with severe decreases in preservation rates, and the high inter-edge variability in image appearance, as well as a strong requirement on the training of the physician properly de-noising an image can be considered a daunting task. The purpose of this advance research is to use a deep learning and image analysis pipeline for multi-exposure satellite image for the segmentation of edges in an image using with hybrid techniques in deep learning and imaging. The literature review of different papers was conducted with different imaging model architectures. The CNN custom model was created for the task, and deep learning technique (CNN) was used with different levels of fine tuning of hybrid satellite image analysis techniques. Screening for high edge filter to identify edges at high accuracy has been under debate. The custom deep learning model architectures were designed to represent different depths. Additionally, deep learning CNN model was created to represent traditional automated image analysis approach. The study also attempts to find solutions to practical deep learning challenges such as low training speed and lack of transparency with an accuracy of 98.17% absolutely.