航空图像的图像分割:综述

Ade Purwanto, Dewi Habsari Budiarti, Fithri Nur Purnamastuti, Irfansyah Yudhi Tanasa, Yomi Guno, Aris Surya Yunata, Mukti Wibowo, Asyaraf Hidayat, Dede Dirgahayu
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引用次数: 0

摘要

由于与人眼的能力相比,识别物体的精度较低,因此多年来一直困扰着研究人员。在过去的十年中,机器学习在航空图像数据处理中的应用成倍增加,其背后的技术也呈指数级发展。其中一项技术是基于图像的目标识别,这在很大程度上依赖于数据计算。为了减少计算量,开发了各种数据分割算法。本文主要综述了航空图像中用于图像识别的各种图像分割技术。回顾了1981年世界各地各种期刊和会议的文献。本文审查了具体的研究问题,以分析图像分割研究随着时间的推移和挑战,研究人员面临的每一种方法。机器学习在分割方法中越来越受欢迎。然而,深度学习通过克服它的许多弱点,积极地在其中发挥了重要作用。在深度学习中使用先进的算法来处理分割,可以推动更有效和准确的数据处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Segmentation in Aerial Imagery: A Review
The problem of distinguishing objects has plagued researchers for many years because of low accuracy compared to human eyes’ capability. In the last decade, the use of Machine Learning in aerial imagery data processing has multiplied, with the technology behind it has also developed exponentially. One of those technologies is image-based object identification, which relies heavily upon data computation. To reduce the computational load, various data segmentation algorithm was developed. This study is focused on reviewing the various image segmentation technology in aerial imagery for image recognition. Literature from as far as 1981 from various journals and conferences worldwide was reviewed. This review examines specific research questions to analyze image segmentation research over time and the challenges researchers face with each method. Machine Learning has gained popularity among segmentation methods. However, Deep Learning has been aggressively put an essential role in it by overcoming many of its weaknesses. The advanced algorithm used in Deep Learning to process the segmentation may drive more efficient and accurate data processing.
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