基于CNN的遥感图像分类模型比较研究

Supritha N, Narasimha Murthy M S
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引用次数: 0

摘要

遥感影像的分类和精度对衡量一个国家的科技发展水平起着至关重要的作用。遥感(RS)可以被解释为一种从远处评估一个表面或一个实体特征的方法。这种识别和分类RS图像数据集的任务可以使用卷积神经网络(CNN)来完成。对于大尺度区域的图像分类,传统的CNN方法产生的是粗糙的地图。为了解决这个问题,可以使用基于对象的CNN方法。基于目标的图像分析可以有效地对高空间分辨率的图像进行分类。深度学习方法提供了自动学习图像空间特征的能力。基于目标尺度的自适应CNN是一种提高高空间分辨率图像分类精度的新技术。为了实现高效的RS图像分类,可以使用一种新的深度学习方法分布式CNN,提高了RS图像分类的准确性。本文比较了三种CNN模型,同时考虑了训练时间和对RS图像进行分类的效率作为度量参数来评估CNN模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification
Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis. Deep learning methods offer the strength of auto learning the spatial features of an image. Object scale based adaptive CNN is a novel technique that can improve the accuracy of image classification of high spatial resolution images. For efficient RS image classification, a novel Deep learning approach called distributed CNN can be used which leads to enhanced accuracy of RS image classification. In this paper, three CNN models have been compared while considering the training time and efficiency to classify RS images as parameters of measure to assess the CNN models.
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