Target detection of remote sensing images based on deep learning method and system

Su-jun Wang, Y. Ping, Gang Chen, Li Yang, Wei Wen, Changzhi Xu, Ying-zhao Shao
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引用次数: 1

Abstract

Abstract: With the rapid growth of remote sensing image data, it is very important to find a way to extract and recognize the target quickly and accurately from the massive remote sensing data. In recent years, the development of deep learning has provided an effective way for target detection of remote sensing images. This paper applies deep learning technology to target detection of remote sensing images, and constructs a target detection system software which integrates sample labeling, data set construction, pretreatment of training sample, training algorithm, migration learning, target recognition and post processing. It provides technical support to the tasks of classification, information extraction and change detection of remote sensing image. The experimental results show that the target recognition system of remote sensing images has high precision in the scene classification and specific target detection of high-resolution remote sensing images.
基于深度学习的遥感图像目标检测方法与系统
摘要:随着遥感图像数据的快速增长,如何从海量遥感数据中快速准确地提取和识别目标显得尤为重要。近年来,深度学习的发展为遥感图像的目标检测提供了有效途径。本文将深度学习技术应用于遥感图像的目标检测,构建了集样本标注、数据集构建、训练样本预处理、训练算法、迁移学习、目标识别和后处理为一体的目标检测系统软件。它为遥感图像的分类、信息提取和变化检测等任务提供了技术支持。实验结果表明,该遥感图像目标识别系统在高分辨率遥感图像的场景分类和特定目标检测方面具有较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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