浏览全部资源
扫码关注微信
纸质出版日期： 2020-01-07 ，
扫 描 看 全 文
徐俊峰,张保明,余东行,林雨准,郭海涛.2020.多特征融合的高分辨率遥感影像飞机目标变化检测.遥感学报,24(1): 37-52
XU Junfeng,ZHANG Baoming,YU Donghang,LIN Yuzhun,GUO Haitao. 2020. Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion. Journal of Remote Sensing(Chinese). 24(1): 37-52
为利用高分辨率遥感影像实现高精度的飞机目标变化检测，提出了一种自适应的多特征融合变化检测与深度学习相结合的方法。首先，通过加权迭代的多元变化检测法获取变化强度图，并结合自适应的直方图统计法自动获取显著的变化与不变化样本；然后，提取多时相影像的光谱、边缘和纹理特征，完成多特征融合的变化检测，并通过形态学处理得到变化图斑；最后，利用训练的NIN(Network in Network)结构的卷积神经网络飞机识别模型，完成变化图斑的类型判别，实现变化飞机的检测。实验结果表明，本文方法在两组数据的正确率分别达到100%和91.89%，均优于对比方法，能实现准确可靠的飞机目标变化检测。
Multi-feature fusion has been widely employed for high-resolution remote sensing images change detection given its ability to reduce the influence of radiation difference
projection errors
and shadows. However
most multi-feature fusion methods depend on artificially designed fusion rules or man-made samples. Meanwhile
many methods for target change detection on bi-temporal images have successfully detected the changed areas yet fail to recognize the number and location of changed targets. To address these limitations
this paper proposes an aircraft target change detection method for high-resolution remote sensing images that combines adaptive multi-feature fusion for change detection with deep learning for target recognition.
First
Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) is applied to generate a change intensity map
and then adaptive histogram statistics are employed to calculate the threshold for the automatic acquisition of conspicuous changed and unchanged samples. Second
the edge and textural features are extracted respectively from multi-temporal images
and a multi-feature fusion change detection is carried out by using a support vector machine and the previous samples. The change polygons are obtained after morphological processing. Third
to prevent a false detection by a single threshold
a multi-threshold strategy is adopted
and raw images from the change polygons are recognized by using the aircraft recognition model that is trained by a few plane slices and the convolutional neural network with network in network.
The experiments for airport images change detection and final aircraft target change detection experiments are performed using two datasets. First
we compare our proposed method is compared with other thresholds and check whether to use multi-features should be used to highlight the effectiveness of our multi-feature fusion change detection method with adaptive samples. To verify its performance
we compare our method with some popular change detection methods are compared
including Multivariate Alteration Detection (MAD)
IR-MAD
principal component analysis
change vector analysis
robust change vector analysis
and iterative slow feature analysis. The experiments show that the overall accuracy of our proposed change detection method outperforms the other compared methods in terms of accuracy and false alarm rate. We obtain our target change detection results based on the change map
and validate the excellent performance of our proposed method based on its accuracy.
To fully use of the spectral
spatial
and textural features of high-resolution remote sensing images
we design an adaptive multi-feature fusion method for change detection that requires less manual work and reduces the influence of radiation difference
projection error
and shadows. We also propose an aircraft target change detection method by combining the multi-feature fusion change detection with target recognition using deep learning. The experimental results validate the excellent performance and reliability of our method.
遥感变化检测多特征融合飞机目标高分辨率遥感影像多元变化检测卷积神经网络
remote sensingchange detectionmulti-feature fusionaircraft targethigh-resolution remote sensing imagesIR-MADconvolutional neural network
Bovolo F and Bruzzone L. 2007. A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on Geoscience and Remote Sensing, 45(1): 218-236 [DOI: 10.1109/TGRS.2006.885408http://dx.doi.org/10.1109/TGRS.2006.885408]
Celik T. 2009. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4): 772-776 [DOI: 10.1109/LGRS.2009.2025059http://dx.doi.org/10.1109/LGRS.2009.2025059]
Chen L Y, Yan M L, Sun X and Wang H Q. 2016. An automatic aircraft detection method based on background prior. Science of Surveying and Mapping, 41(3): 69-74, 33
陈丽勇, 闫梦龙, 孙显, 王宏琦. 2016. 一种基于背景先验的飞机目标检测方法. 测绘科学, 41(3): 69-74, 33[DOI: 10.16251/j.cnki.1009-2307.2016.03.014http://dx.doi.org/10.16251/j.cnki.1009-2307.2016.03.014]
Du P J and Liu S C. 2012. Change detection from multi-temporal remote sensing images by integrating multiple features. Journal of Remote Sensing, 16(4): 663-677
杜培军, 柳思聪. 2012. 融合多特征的遥感影像变化检测. 遥感学报, 16(4): 663-677[DOI: 10.11834/jrs.20121168http://dx.doi.org/10.11834/jrs.20121168]
Ekblad U and Kinser J M. 2004. Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars, and nuclear explosion tests. Signal Processing, 84(7): 1131-1146 [DOI: 10.1016/j.sigpro.2004.03.012http://dx.doi.org/10.1016/j.sigpro.2004.03.012]
Hao R, Xu J F, Wang Q B and Zhang B M. 2016. Change detection method using multi-feature fusion based on BP neural network. Hydrographic Surveying and Charting, 36(1): 79-82
郝睿, 徐俊峰, 王庆宝, 张保明. 2016. 基于BP神经网络的多特征融合变化检测方法. 海洋测绘, 36(1): 79-82[DOI: 10.3969/j.issn.1671-3044.2016.01.020http://dx.doi.org/10.3969/j.issn.1671-3044.2016.01.020]
Hsieh J W, Chen J M, Chuang C H and Fan K C. 2005. Aircraft type recognition in satellite images. IEE Proceedings - Vision, Image and Signal Processing, 152(3): 307-315 [DOI: 10.1049/ip-vis:20049020http://dx.doi.org/10.1049/ip-vis:20049020]
Li L, Shu N, Wang K and Gong Y. 2014. Change detection method for remote sensing images based on multi-features fusion. Acta Geodaetica et Cartographica Sinica, 43(9): 945-953
李亮, 舒宁, 王凯, 龚龑. 2014. 融合多特征的遥感影像变化检测方法. 测绘学报, 43(9): 945-953[DOI: 10.13485/j.cnki.11-2089.2014.0138http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0138]
Li X J, Wang C L, Li Y and Sun H. 2016. Optical remote sensing object detection based on fused feature contrast of subwindows. Optics and Precision Engineering, 24(8): 2067-2077
李湘眷, 王彩玲, 李宇, 孙皓. 2016. 窗口融合特征对比度的光学遥感目标检测. 光学 精密工程, 24(8): 2067-2077 [DOI: 10.3788/OPE.20162408.2067http://dx.doi.org/10.3788/OPE.20162408.2067]
Lin M, Chen Q and Yan S C. 2013. Network in network. arXiv preprint arXiv:1301.3557
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. The Netherlands: Springer: 21-37 [DOI: 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2]
Maggiori E, Tarabalka Y, Charpiat G and Alliez P. 2017. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 645-657 [DOI: 10.1109/TGRS.2016.2612821http://dx.doi.org/10.1109/TGRS.2016.2612821]
Nielsen A A, Conradsen K and Simpson J J. 1998. Multivariate Alteration Detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote Sensing of Environment, 64(1): 1-19 [DOI: 10.1016/S0034-4257(97)00162-4http://dx.doi.org/10.1016/S0034-4257(97)00162-4]
Nielsen A A. 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing, 16(2): 463-478 [DOI: 10.1109/TIP.2006.888195http://dx.doi.org/10.1109/TIP.2006.888195]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 779-788 [DOI: 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91]
Ren S Q, He K M, Girshick R and Sun J. 2015. Faster R-CNN: towards real-time object detection with region proposal networks//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada: ACM: 91-99
Sun H, Sun X, Wang H Q, Li Y and Li X J. 2012. Automatic target detection in high-resolution remote sensing images using spatial sparse coding Bag-of-Words model. IEEE Geoscience and Remote Sensing Letters, 9(1): 109-113 [DOI: 10.1109/LGRS.2011.2161569http://dx.doi.org/10.1109/LGRS.2011.2161569]
Thonfeld F, Feilhauer H, Braun M and Menz G. 2016. Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data. International Journal of Applied Earth Observation and Geoinformation, 50: 131-140 [DOI: 10.1016/j.jag.2016.03.009http://dx.doi.org/10.1016/j.jag.2016.03.009]
Wang W S, Nie T, Fu T J, Ren J Y and Jin L X. 2017. A novel method of aircraft detection based on high-resolution panchromatic optical remote sensing images. Sensors, 17(5): 1047 [DOI: 10.3390/s17051047http://dx.doi.org/10.3390/s17051047]
Wu C, Du B and Zhang L P. 2014. Slow feature analysis for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2858-2874 [DOI: 10.1109/TGRS.2013.2266673http://dx.doi.org/10.1109/TGRS.2013.2266673]
Xiang S W, Wen G J and Gao F. 2016. Knowledge driven change detection method for aircraft targets. Remote Sensing for Land and Resources, 28(4): 77-82
项盛文, 文贡坚, 高峰. 2016. 知识驱动下的飞机目标变化检测方法. 国土资源遥感, 28(4): 77-82[DOI: 10.6046/gtzyyg.2016.04.12http://dx.doi.org/10.6046/gtzyyg.2016.04.12]
Xiao P F, Zhang X L, Wang D G, Yan M, Feng X Z and Kelly M. 2016. Change detection of built-up land: a framework of combining pixel-based detection and object-based recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 119: 402-414 [DOI: 10.1016/j.isprsjprs.2016.07.003http://dx.doi.org/10.1016/j.isprsjprs.2016.07.003]
Xu J F, Guo H T, Zhang R and Liu P C. 2015. An adaptive edge detection method for multi-spectral image combining quaternion and histogram. Journal of Geomatics Science and Technology, 32(1): 61-65
徐俊峰, 郭海涛, 张锐, 刘攀成. 2015. 一种结合四元数与直方图的多光谱图像自适应边缘检测方法. 测绘科学技术学报, 32(1): 61-65[DOI: 10.3969/j.issn.1673-6338.2015.01.013http://dx.doi.org/10.3969/j.issn.1673-6338.2015.01.013]
Zhang L P, Zhang L F and Du B. 2016. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2): 22-40 [DOI: 10.1109/MGRS.2016.2540798http://dx.doi.org/10.1109/MGRS.2016.2540798]
Zhang X L, Chen X W, Li F and Yang T. 2017. Change detection method for high resolution remote sensing images using deep learning. Acta Geodaetica et Cartographica Sinica, 46(8): 999-1008
张鑫龙, 陈秀万, 李飞, 杨婷. 2017. 高分辨率遥感影像的深度学习变化检测方法. 测绘学报, 46(8): 999-1008[DOI: 10.11947/j.AGCS.2017.20170036http://dx.doi.org/10.11947/j.AGCS.2017.20170036]
Zhao A, Fu K, Sun H, Sun X, Li F, Zhang D B and Wang H Q. 2017. An effective method based on ACF for aircraft detection in remote sensing images. IEEE Geoscience and Remote Sensing Letters, 14(5): 744-748 [DOI: 10.1109/LGRS.2017.2677954http://dx.doi.org/10.1109/LGRS.2017.2677954]
Zhao M and Zhao Y D. 2018. Object-oriented and multi-feature hierarchical change detection based on CVA for high-resolution remote sensing imagery. Journal of Remote Sensing, 22(1): 119-131
赵敏, 赵银娣. 2018. 面向对象的多特征分级CVA遥感影像变化检测. 遥感学报, 22(1): 119-131[DOI: 10.11834/jrs.20186293http://dx.doi.org/10.11834/jrs.20186293]
相关作者
相关机构
京公网安备11010802024621