基于改进U-Net卷积网络的卫星图像分割

N. Subraja, D. Venkatasekhar
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引用次数: 3

摘要

卫星图像中的目标检测是近年来备受关注的一个重要而复杂的问题,在广泛的应用中发挥着重要的作用。深度学习技术在计算机想象力和先见之明领域得到大量应用后,目前正在卫星图像的背景下进行研究,以实现物体识别、物体跟踪、物体分类、航空/卫星图像的语义分割等独特功能。尽管开展了与卫星/航空图像目标检测相关的各种评估研究,但本观察提供了使用深度学习的卫星图像目标检测学科的最新发展评估。本文详细阐述了使用改进的U-Net卷积网络对道路、建筑物、太阳能电池板和车辆的检测,与以往的方法相比,取得了更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Satellite Image Segmentation using Modified U-Net Convolutional Networks
The object detection in satellite imagery is a primary and elaborate one receiving lot of interest in latest years and performs an essential function for wide range of applications. After the massive fulfillment of Deep learning techniques in computer imaginative and prescient discipline, they're presently being studied in the context of satellite imagery for unique functions like object identification, object tracking, object classification, semantic segmentation of aerial/satellite images. Although diverse assessment research associated with object detection from satellite/aerial imagery are carried out, this observation provides an assessment of the latest development in the discipline of object detection from satellite imagery with the use of deep learning. This paper elaborates the detection of roads, buildings, solar panels and vehicles using Modified U-Net Convolutional networks and achieves more accuracy compared to the previous ones.
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