Aircraft detection in remote sensing imagery with multi-scale feature fusion convolutional neural networks

doi:10.11947/j.AGCS.2019.20180398

Abstract

Abstract: Aircraft detection in remote sensing images (RSIs) is a meaningful task. There are many problems in current detection methods, such as low accuracy in complex background and dense aircraft area, especially for small-scale aircraft. To solve these problems, an end-to-end aircraft detection method named MultDet is proposed in this paper. Based on single shot multibox detector (SSD), a lightweight baseline Network is used to extract multi-scale features for its powerful ability in feature extraction. To obtain the feature maps with enriched representation power, then the multi-scale deconvolution feature fusion block is designed. We add the high-level features with rich semantic information to the low-level features via deconvolution fusion block. In order to locate aircraft of various scales more accurately, a series of aspect ratios of default boxes are set to better match aircraft shapes and combine predictions deduced from feature maps of different layers. The quantitative comparison analysis are carried out on the challenging UCAS-AOD data set. The experimental results demonstrate that the proposed method is accurate and robust for multi-scale aircraft detection, and achieves 94.8% AP(average precision) at the speed of 0.050 0 s/img with the input size 512×512 using a single Nvidia Titan Xp GPU.

Key words: remote sensing images, aircraft detection, feature fusion, multi-scale features

CLC Number:

P237

YAO Qunli, HU Xian, Lei Hong. Aircraft detection in remote sensing imagery with multi-scale feature fusion convolutional neural networks[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10): 1266-1274.

References 25

[1]	CHENG Gong, ZHOU Peicheng, HAN Junwei. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12):7405-7415.
[2]	HAN Junwei, ZHANG Dingwen, CHENG Gong, et al. Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(6):3325-3337.
[3]	CHENG Gong, HAN Junwei, ZHOU Peicheng, et al. Multi-class geospatial object detection and geographic image classification based on collection of part detectors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 98(12):119-132.
[4]	HE Chu, TU Mingxia, XIONG Dehui, et al. Adaptive component selection-based discriminative model for object detection in high-resolution SAR imagery[J]. ISPRS International Journal of Geo-Information, 2018, 7(2):72.
[5]	DIAO Wenhui, SUN Xian, ZHENG Xinwei, et al. Efficient saliency-based object detection in remote sensing images using deep belief networks[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2):137-141.
[6]	DU Bo, ZHANG Yuxiang, ZHANG Liangpei, et al. Beyond the sparsity-based target detector:a hybrid sparsity and statistics-based detector for hyperspectral images[J]. IEEE Transactions on Image Processing, 2016, 25(11):5345-5357.
[7]	LI Xuelong, MOU Lichao, LU Xiaoqiang. Scene parsing from an MAP perspective[J]. IEEE Transactions on Cybernetics, 2015, 45(9):1876-1886.
[8]	ZHANG Libao, ZHANG Yingying. Airport detection and aircraft recognition based on two-layer saliency model in high spatial resolution remote-sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4):1511-1524.
[9]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada:ACM, 2015:91-99.
[10]	DAI Jifeng, LI Yi, HE Kaiming, et al. R-FCN:object detection via region-based fully convolutional networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain:ACM, 2016:379-387.
[11]	REDMON J, FARHADI A. YOLO9000:better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu:IEEE, 2017:6517-6525.
[12]	LIU Wei, ANGUELOV D, ERHAN D, et al. SSD:single shot MultiBox detector[C]//Proceedings of the 14th European Conference On Computer Vision. Amsterdam, The Netherlands:Springer, 2016:21-37.
[13]	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu:IEEE, 2017:936-944.
[14]	HAN Xiaobing, ZHONG Yanfei, ZHANG Liangpei. An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery[J]. Remote Sensing, 2017, 9(7):666.
[15]	CAI Bowen, JIANG Zhiguo, ZHANG Haopeng, et al. Airport detection using end-to-end convolutional neural network with hard example mining[J]. Remote Sensing, 2017, 9(11):1198.
[16]	GUO Wei, YANG Wen, ZHANG Haijian, et al. Geospatial object detection in high resolution satellite images based on multi-scale convolutional neural network[J]. Remote Sensing, 2018, 10(1):131.
[17]	邓志鹏, 孙浩, 雷琳, 等. 基于多尺度形变特征卷积网络的高分辨率遥感影像目标检测[J]. 测绘学报, 2018, 47(9):1216-1227. DOI:10.11947/j.AGCS.2018.20170595. DENG Zhipeng, SUN Hao, LEI Lin, et al. Object detection in remote sensing imagery with multi-scale deformable convolutional networks[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9):1216-1227. DOI:10.11947/j.AGCS.2018.20170595.
[18]	CHEN Zhong, ZHANG Ting, OUYANG Chao. End-to-end airplane detection using transfer learning in remote sensing images[J]. Remote Sensing, 2018, 10(1):139.
[19]	SHEN Zhiqiang, LIU Zhuang, LI Jianguo, et al. DSOD:learning deeply supervised object detectors from scratch[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017:1937-1945.
[20]	BELL S, ZITNICK C L, BALA K, et al. Inside-outside net:detecting objects in context with skip pooling and recurrent neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas:IEEE, 2016:2874-2883.
[21]	KONG Tao, SUN Fuchun, YAO Anbang, et al. RON:reverse connection with objectness prior networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu:IEEE, 2017:5244-5252.
[22]	ZHU Haigang, CHEN Xiaogang, DAI Weiqun, et al. Orientation robust object detection in aerial images using deep convolutional neural network[C]//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City:IEEE, 2015:3735-3739.
[23]	LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston:IEEE, 2015:3431-3440.
[24]	梁华, 宋玉龙, 钱锋等. 基于深度学习的航空对地小目标检测[J]. 液晶与显示, 2018, 33(9):793-800. LIANG Hua, SONG Yulong, QIAN Feng, et al. Detection of small target in aerial photography based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays, 2018, 33(9):793-800.
[25]	XIA Guisong, BAI Xiang, DING Jian, et al. DOTA:a large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018:3974-3983.