基于I-FCN模型的城市高分辨率遥感影像植被信息提取
|
摘要
为了提高城市高分辨率遥感影像中植被信息的提取精度,提出一种改进的全卷积神经网络模型,通过大量的训练数据获得最佳模型参数,进行植被信息的提取,并与支持向量机、面向对象法、经典的FCN模型方法提取的植被信息进行对比分析。结果表明:提出的网络模型不但能够有效缓解"椒盐现象",还能保证小面积的植被提取与植被区域边界的准确性。该方法可自动综合多种特征,所以可有效减少植被像元的误分与漏分现象,提高植被提取精度。
In order to improve the extraction of urban vegetation from high-resolution remote sensing image,an novel full convolutional neural network model was proposed. The best model parameters were obtained through a large amount of training data, and the vegetation information was extracted. The vegetation information extracted by support vector machine, object-oriented method and classical FCN model method was compared and analyzed. The results show that the proposed network model can not only effectively alleviate the "salt and pepper phenomenon", but also ensure the accuracy of small-area vegetation extraction and vegetation area boundaries.The method can automatically integrate multiple features, so it can effectively reduce the misclassification and leakage of vegetation pixels and improve the accuracy of vegetation extraction.
In order to improve the extraction of urban vegetation from high-resolution remote sensing image,an novel full convolutional neural network model was proposed. The best model parameters were obtained through a large amount of training data, and the vegetation information was extracted. The vegetation information extracted by support vector machine, object-oriented method and classical FCN model method was compared and analyzed. The results show that the proposed network model can not only effectively alleviate the "salt and pepper phenomenon", but also ensure the accuracy of small-area vegetation extraction and vegetation area boundaries.The method can automatically integrate multiple features, so it can effectively reduce the misclassification and leakage of vegetation pixels and improve the accuracy of vegetation extraction.
引文
[1]王伯荪.城市植被与城市植被学[J].中山大学学报(自然科学版),1998,37(4):9-12.
[2]唐昊.基于蜂群算法优化的支持向量机植被提取研究[J].城市勘测,2018(2):86-89.
[3]陈仁喜,王成芳.城市高分辨率影像绿地植被识别研究进展[J].遥感信息,2013,28(3):119-125.
[4]温礼,王锦,张所林,等.珠江源自然保护区湿地遥感监测与动态变化研究[J].西南林业大学学报(自然科学),2018,38(6):159-165.
[5]孙亚丽,周筑,黄海燕,等.基于卫星遥感影像的森林资源二类调查[J].西部林业科学,2017,46(2):150-153.
[6]刘晓琴,胡云岗,侯妙乐,等.顾及资三卫星全色与多光谱数据的植被信息提取研究[J].北京建筑大学学报,2018,34(1):53-57.
[7]唐天琦,程良勇,王海,等.面向对象的高分辨率遥感影像植被信息提取研究[J].信息与电脑(理论版),2017(14):30-32.
[8]Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.
[9]Xu Y Y,Wu L,Xie Z,et al.Building extraction in very high resolution remote sensing imagery using deep learning and guided filters[J].Remote Sensing,2018,10(1):144.
[10]Yu F,Koltun V.Multi-scale context aggregation by dilated convolutions[J/OL]ar Xiv,2016:1511.07122[2016-04-30].https://arxiv.org/abs/1511.07122
[11]Sherrah J.Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery.[J/OL]ar Xiv,2016:1606.02585[2016-06-08].https://arxiv.org/abs/1606.02585.
[12]Wang P Q,Chen P F,Yuan Y,et al.Understanding convolution for semantic segmentation[C]//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision,March 12-15,2018.Washington DC:IEEE Computer Society,2018:1451-1460.
[13]He K M,Zhang X Y,Ren S Q,et al.Delving deep into rectifiers:surpassing human-level performance on Image Net classification[C]//Proceedings of 2015 IEEEInternational Conference on Computer Vision,December 7-13,2015.Washington DC:IEEE Computer Society,2015:1026-1034.
[2]唐昊.基于蜂群算法优化的支持向量机植被提取研究[J].城市勘测,2018(2):86-89.
[3]陈仁喜,王成芳.城市高分辨率影像绿地植被识别研究进展[J].遥感信息,2013,28(3):119-125.
[4]温礼,王锦,张所林,等.珠江源自然保护区湿地遥感监测与动态变化研究[J].西南林业大学学报(自然科学),2018,38(6):159-165.
[5]孙亚丽,周筑,黄海燕,等.基于卫星遥感影像的森林资源二类调查[J].西部林业科学,2017,46(2):150-153.
[6]刘晓琴,胡云岗,侯妙乐,等.顾及资三卫星全色与多光谱数据的植被信息提取研究[J].北京建筑大学学报,2018,34(1):53-57.
[7]唐天琦,程良勇,王海,等.面向对象的高分辨率遥感影像植被信息提取研究[J].信息与电脑(理论版),2017(14):30-32.
[8]Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.
[9]Xu Y Y,Wu L,Xie Z,et al.Building extraction in very high resolution remote sensing imagery using deep learning and guided filters[J].Remote Sensing,2018,10(1):144.
[10]Yu F,Koltun V.Multi-scale context aggregation by dilated convolutions[J/OL]ar Xiv,2016:1511.07122[2016-04-30].https://arxiv.org/abs/1511.07122
[11]Sherrah J.Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery.[J/OL]ar Xiv,2016:1606.02585[2016-06-08].https://arxiv.org/abs/1606.02585.
[12]Wang P Q,Chen P F,Yuan Y,et al.Understanding convolution for semantic segmentation[C]//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision,March 12-15,2018.Washington DC:IEEE Computer Society,2018:1451-1460.
[13]He K M,Zhang X Y,Ren S Q,et al.Delving deep into rectifiers:surpassing human-level performance on Image Net classification[C]//Proceedings of 2015 IEEEInternational Conference on Computer Vision,December 7-13,2015.Washington DC:IEEE Computer Society,2015:1026-1034.