北京测绘2024，Vol.38Issue(9)：1341-1345,5.DOI:10.19580/j.cnki.1007-3000.2024.09.018
基于改进密集连接网络的土地卫片场景分类方法
Satellite image classification method for land scenes based on improved densely connected network
摘要
Abstract
In order to improve the efficiency and accuracy of satellite image identification of land scenes,a Laplace pyramid-densely connected convolutional network(Lap-DenseNet)model was constructed to identify and classify land scenes.Lap in the Lap-DenseNet model adopted a three-layer pyramid structure,while DenseNet used a 169-layer structure.The constructed Lap-DenseNet model was applied to satellite image classification containing six types of land scenes.The results show that the number of iterations in the training set of the Lap DenseNet model should not be too much.Otherwise,the classification performance will be reduced due to overfitting.The best classification performance is achieved when the number of iterations is 200.The Lap DenseNet model has the best classification performance for rural roads but has poor classification performance for cultivated land with a green background,uncultivated land,and agricultural land with a green background.The average classification accuracy for the six types of land scenes is 93.66%.Compared with six scene classification methods such as Google convolutional network(GoogLeNet),convolutional network for fast feature embedding(CaffeNet),dense connectivity-based two-stream deep feature fusion convolutional networks(TEX-TS-Net),convolutional network of additional resources based on VGG16(ARCNet-VGG16),capsule convolutional networks based on Inception-v3(Inception-v3-CapsNet),and convolutional networks based on global context space attention and dense connections(GCSANet),the Lap DenseNet model has the best classification performance and can be reasonably applied in satellite image classification work of land scenes.关键词
土地卫片/场景分类/拉普拉斯金字塔-密集连接卷积网络(Lap-DenseNet)模型/迭代次数/分类准确率Key words
satellite images of land scenes/scene classification/Laplace pyramid-densely connected convolutional network(Lap-DenseNet)model/number of iterations/classification accuracy分类
天文与地球科学引用本文复制引用
吴志斌..基于改进密集连接网络的土地卫片场景分类方法[J].北京测绘,2024,38(9):1341-1345,5.基金项目
北京市自然科学基金(4214069) （4214069）
