摘要
目的卫星图像往往目标、背景复杂而且带有噪声,因此使用人工选取的特征进行卫星图像的分类就变得十分困难。提出一种新的使用卷积神经网络进行卫星图像分类的方案。使用卷积神经网络可以提取卫星图像的高层特征,进而提高卫星图像分类的识别率。方法首先,提出一个包含六类图像的新的卫星图像数据集来解决卷积神经网络的有标签训练样本不足的问题。其次,使用了一种直接训练卷积神经网络模型和3种预训练卷积神经网络模型来进行卫星图像分类。直接训练模型直接在文章提出的数据集上进行训练,预训练模型先在ILSVRC(the Image Net large scale visual recognition challenge)-2012数据集上进行预训练,然后在提出的卫星图像数据集上进行微调训练。完成微调的模型用于卫星图像分类。结果提出的微调预训练卷积神经网络深层模型具有最高的分类正确率。在提出的数据集上,深层卷积神经网络模型达到了99.50%的识别率。在数据集UC Merced Land Use上,深层卷积神经网络模型达到了96.44%的识别率。结论本文提出的数据集具有一般性和代表性,使用的深层卷积神经网络模型具有很强的特征提取能力和分类能力,且是一种端到端的分类模型,不需要堆叠其他模型或分类器。在高分辨卫星图像的分类上,本文模型和对比模型相比取得了更有说服力的结果。
Objective Satellite imagery classification is a task that uses classification models to divide a set of satellite images into several classes. The satellite images discussed in this paper are collected from the Quickbird satellite imagery dataset. Satellite images are divided into six classes, namely, airplanes, dense residential areas, harbors, intersections, overpasses, and parking lots. Generally, the task of satellite imagery classification is difficult because of the complex targets and backgrounds in satellite images. Traditional methods, such as the artificial neural networks and support vector machines, usually use low-level and manually selected features. These features are insufficient and cannot represent the multi-level and intrinsic features of satellite images. Simultaneously, obtaining high accuracy is difficult through the classification methods, which use low-level features. Some deep learning methods use pre-trained convolutional neural networks to extract the high-level features of satellite images and some classifier to classify satellite images. These methods can improve their performance than the traditional methods. However, these methods ignore the inherent classification capability of convolutional neural networks because considerable labeled training data of satellite images are required to train a convolutional neural network, which could extract features and classify images simultaneously; however, training data are limited in practice. Other methods use a stack of shallow convolutional neural networks to classify satellite images. However, the stack of low-level features remains insufficient representative to substantially improve the classification accuracy of satellite image. In this paper, a new approach using deep convolutional neural networks is presented to improve the classification accuracy for satellite imagery. The classification accuracy of satellite images could be improved using the deep features extracted by convolutional neural networks. Method An end-to-end training an
出处
《中国图象图形学报》
CSCD
北大核心
2017年第7期996-1007,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(41171338
41471280
61401265)~~
关键词
卫星图像
分类
卷积神经网络
模型
特征
satellite imagery
classification
convolutional neural networks
model
feature
