一种基于改进FCN的多光谱图像建筑物识别方法
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摘要
多光谱图像的建筑物目标在不同尺度下具有不同特征,利用传统全卷积神经网络(FCN)进行识别时精度较低。为此,提出一种基于改进FCN的多光谱图像建筑物识别方法。通过旋转图像进行训练集扩充,从网络的第1层～第12层提取图像在4个旋转角度和不同尺度下的低层特征,将其归一化为同样尺寸的图像后提取更高层特征,以实现对多光谱图像建筑物的精确识别。实验结果表明,相比传统FCN方法,该方法能够提高识别的精确率与召回率。
Building targets in multispectral image have different characteristics at different scales,and the recognition accuracy of traditional Full Convolution Neural network( FCN) is low. Therefore,a building recognition method for multispectral image based on improved FCN is proposed. After expanding the training set by rotating images,the low-level features of images at four rotating angles and at different scales are extracted from the first to the tw elfth layers of the network. After normalizing them into images of the same size,the higher-level features are extracted to realize the accurate recognition of multispectral image buildings. Experimental results show that compared with the traditional FCN method,this method can improve the recognition accuracy and recall rate.
Building targets in multispectral image have different characteristics at different scales,and the recognition accuracy of traditional Full Convolution Neural network( FCN) is low. Therefore,a building recognition method for multispectral image based on improved FCN is proposed. After expanding the training set by rotating images,the low-level features of images at four rotating angles and at different scales are extracted from the first to the tw elfth layers of the network. After normalizing them into images of the same size,the higher-level features are extracted to realize the accurate recognition of multispectral image buildings. Experimental results show that compared with the traditional FCN method,this method can improve the recognition accuracy and recall rate.
引文
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[2]BAI X,ZHANG H,ZHOU J.VHR object detection based on structural feature extraction and query expansion[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(10):6508-6520.
[3]SENARAS C,OZAY M,VURAL F T Y.Building detection w ith decision fusion[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,6(3):1295-1304.
[4]CHEN Y,NASRABADI N M,TRAN T D.Simultaneous joint sparsity model for target detection in hyperspectral imagery[J].IEEE Geoscience and Remote Sensing Letters,2011,8(4):676-680.
[5]HAN J,ZHANG D,CHENG G,et al.Object detection in optical remote sensing images based on w eakly supervised learning and high-level feature learning[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(6):3325-3337.
[6]LI E,FEMIANI J,XU S,et al.Robust rooftop extraction from visible band images using higher order CRF[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(8):4483-4495.
[7]MA L,CRAWFORD M M,TIAN J.Local manifold learning-based-nearest-neighbor for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(11):4099-4109.
[8]TANG J,DENG C,HUANG G B,et al.Compresseddomain ship detection on spaceborne optical image using deep neural netw ork and extreme learning machine[J].IEEE Transactions on Geoscience and Remote Sensing,2014,53(3):1174-1185.
[9]SLAVKOVIKJ V,VERSTOCKT S,NEVE V D,et al.Hyperspectral image classification with convolutional neural networks[C]//Proceedings of the 23rd ACM International Conference on Multimedia.New York,USA:ACM Press,2015:1159-1162.
[10]周敏,史振威,丁火平.遥感图像飞机目标分类的卷积神经网络方法[J].中国图象图形学报,2017,22(5):702-708.
[11]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2017,39(7):640-651.
[12]TANG P,WANG H,KWONG S.G-MS2F:GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition[J].Neurocomputing,2016,225:188-197.
[13]BENGIO Y.Neural networks:tricks of the trade[M].Berlin,Germany:Springer,2012.
[14]HARIHARAN B,ARBELEZ P,GIRSHICK R,et al.Hypercolumns for object segmentation and fine-grained localization[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:447-456.
[15]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of International Conference on Neural Information Processing Systems.Washington D.C.,USA:IEEE Press,2015:91-99.
[16]厉智,孙玉宝,王枫,等.基于深度卷积神经网络的服装图像分类检索算法[J].计算机工程,2016,42(11):309-315.
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