Review of CNN in aerial image processing

References

• Wuttichai B, Yumin T, Yinghua Y, et al. A deep learning approach on building detection from unmanned aerial vehicle-based images in riverbank monitoring. Sensors. 2018;18(11):3921–3933.
   PubMed Web of Science ®Google Scholar
• Zhengxia Z, Zhenwei S. Random access memories: a new paradigm for target detection in high resolution aerial remote sensing images. IEEE Trans Image Proc. 2018;27(3):1100–1111.
   PubMed Web of Science ®Google Scholar
• Griffiths D, Boehm J. Rapid object detection systems, utilising deep learning and unmanned aerial systems (UAS) for civil engineering applications. ISPRS TC II Mid-term Sym, Riva del Garda, Italy, Proceedings of ISPRS 2018; 2018, 391–398.
   Google Scholar
• Kasthurirangan G, Hoda G, Akash V, et al. Crack damage detection in unmanned aerial vehicle images of civil infrastructure using pre-trained deep learning model. Int J Traffic Trans Eng. 2018;8(1):1–14.
   Google Scholar
• Diogo D, Francesco N, Norman K, et al. Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Rem Sens. 2018;10(10):1636–1661.
   Google Scholar
• Dimitrios M, Mihai D, Thomas E, et al. Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci Rem Sens Let. 2016;13(1):105–109.
   Web of Science ®Google Scholar
• Xiangtao Z, Yuan Y, Lu X. A deep scene representation for aerial scene classification. IEEE Trans: Geosci Rem Sens. 2019;57(7):4799–4809.
   Web of Science ®Google Scholar
• Lillesand TM, Kiefer RW, Chipman J. Remote sensing and image interpretation. New York (NY): Wiley; 2008.
   Google Scholar
• Shaw G, Manolakis D. Signal processing for hyperspectral image exploitation. IEEE Sig Proc Mag. 2002;19(1):12–16.
   Web of Science ®Google Scholar
• Ishimwe R, Abutaleb K, Ahmed F. Applications of thermal imaging in agriculture – A review. Adv in Rem Sens. 2014;3(2014):128–140.
   Google Scholar
• Fedra T, Andres F, Carlos S, et al. Corn classification using deep learning with UAV imagery. An operational proof of concept. IEEE 1st ColCACI, IEEE, Medellin, Colombia, 2018.
   Google Scholar
• Mohammad R, Yun Z, Rakesh M, et al. Using a VGG-16 network for individual tree species detection with an object-based approach. 10th IAPR Work. Pat. Rec. in Rem. Sensg (PRRS), IEEE, Beijing, China, 2018.
   Google Scholar
• Calhoon S, Conwell W Y. Image processing methods and arrangements, Google Patents, USA (2018).
   Google Scholar
• Saxena L, Armstrong L. A survey of image processing techniques for agriculture. Proceedings of AFITA; Perth, Australia, 405–418, 2014.
   Google Scholar
• Arti S, Baskar G, et al. Machine learning for high-throughput stress phenotyping in plants. Tre Plant Sci. 2016;21(2):110–124.
   PubMed Web of Science ®Google Scholar
• Alfatni MSM, Shariff ARM, Shafri HZM, et al. Oil palm fruit bunch grading system using red, green and blue digital number. J App Sci. 2008;8(8):1444–1452.
   Google Scholar
• Chen C, Baochang Z, et al. Land-use scene classification using multi-scale completed local binary patterns. Sig ima vid Proc. 2016;10(4):745–752.
   Web of Science ®Google Scholar
• Kamusoko Courage. Importance of remote sensing and land change modeling for urbanization studies. Urban development in Asia and Africa. Singapore: Springer; 2017. p. 3–10.
   Google Scholar
• Kerkech M, Hafiane A, Canals R. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Comp Electr Agric. 2018;155:237–243.
   Web of Science ®Google Scholar
• Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. Proceedings of International Conference on Mac. Lear (ICML-10); Haifa, Israel, 807–814, 2010.
   Google Scholar
• White AR. Human expertise in the interpretation of remote sensing data: A cognitive task analysis of forest disturbance attribution. Int J App Ear Obser Geoinformation. 2019;74:37–44.
   Web of Science ®Google Scholar
• Hassan M, Golam R AM, Uddin M Z, et al. Human emotion recognition using deep belief network architecture. Infor Fusion. 2019;51:10–18.
   Web of Science ®Google Scholar
• Rig D, Emanuela P, Emanuele M, et al. Convolutional neural network for finger-vein-based biometric identification. IEEE Trans Infor Forensic Sec. 2019;14(2):360–373.
   Web of Science ®Google Scholar
• Calvo-Zaragoza J, Gallego A. A selectional auto-encoder approach for document image binarization. Com Ver Pattern Rec. 2019;86:37–47.
   Web of Science ®Google Scholar
• He K, Xiangyu Z, Shaoqing R, et al. Deep residual learning for image recognition. IEEE Conference on Computer Vision Pattern Recognition (CVPR), Las Vegas, NV, USA, 770–778, 2016.
   Google Scholar
• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition, arXiv preprint, arXiv:1409.1556 (2014).
   Google Scholar
• Wenlu Z, Rongjian L, Tao Z, et al. Deep model based transfer and multi-task learning for biological image analysis. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1475–1484, 2015.
   Google Scholar
• Weixun Z, Zhenfeng S, Chunyuan D, et al. High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder. Rem. Sens Lett. 2015;6(10):775–783.
   Web of Science ®Google Scholar
• Agostinelli F, Hoffman M, Sadowski P, et al. Learning activation functions to improve deep neural networks, arXiv preprint, arXiv:1412.6830, 2014.
   Google Scholar
• Metin NG, Heang-Ping C. Optimal neural network architecture selection: improvement in computerized detection of microcalcifications. Acad Radiol. 2002;9(4):420–429.
   PubMed Web of Science ®Google Scholar
• Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Advances in Neural information Processing System; Lake Tahoe, Nevada, 1097–1105, 2012.
   Google Scholar
• Kaiming H, Xiangyu Z, Shaoqing R, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. Proceedings of IEEE International Conference on Computer Vision; Santiago, Chile , 1026–1034, 2015.
   Google Scholar
• Hilal E, Yusuf C A, Mustafa S, et al. Early and late level fusion of deep convolutional neural networks for visual concept recognition. Int J Sema Comp. 2016;10(3):347–363.
   Google Scholar
• Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–2324.
   Web of Science ®Google Scholar
• Deng J, Dong W, Socher R. Imagenet: a large-scale hierarchical image database. IEEE Conference on Computer Vision Pattern Recognition; New York, IEEE Press, 248–255, 2009.
   Google Scholar
• Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neu Net. 1994;5(2):157–166.
   PubMed Web of Science ®Google Scholar
• Szegedy C, Wei L, Yangqing J, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9, 2015.
   Google Scholar
• Min L, Chen Q, Yan S. Network in network, arXiv preprint, arXiv, 2013. 1312.4400.
   Google Scholar
• Tayara H, Chong KT. Object detection in very high-resolution aerial images using one-stage densely connected feature pyramid network. Sens. 2018;18(10):3341–3358.
   PubMed Web of Science ®Google Scholar
• Gong C, Ceyuan Y, Xiwen Y, et al. When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Trans Geosci Rem Sens. 2018;56(5):2811–2821.
   Web of Science ®Google Scholar
• Marco C, Giovanni P, Carlo S, et al. Land use classification in remote sensing images by convolutional neural networks, arXiv preprint, arXiv:1508.00092 (2015).
   Google Scholar
• Guofeng S, Wen Y, Tao X, et al. High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int J Rem Sens. 2012;33(8):2395–2412.
   Web of Science ®Google Scholar
• Gong C, Junwei H, Xiaoqiang L. Remote sensing image scene classification: benchmark and state of the art. Proc IEEE. 2017;105(10):1865–1883.
   Web of Science ®Google Scholar
• GuiSong X, Jingwen H, et al. AID: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans Geosci Rem Sens Let. 2017;55(7):3965–3981.
   Web of Science ®Google Scholar
• Gong C, Junwei H, Peicheng Z, et al. Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS J Photog Rem Sens Let. 2014;98:119–132.
   Web of Science ®Google Scholar
• Razakarivonyb S, Jurie F. Vehicle detection in aerial imagery: a small target detection benchmark. J Vis Commu Ima Rep. 2016;34:187–203.
   Web of Science ®Google Scholar
• Franklin T, Brian C, Craig P, et al. Overhead imagery research data set – an annotated data library & tools to aid in the development of computer vision algorithms. AIPR 2009, DC: Washington IEEE, 2010.
   Google Scholar
• Rottensteiner F, Sohn G, Jung J, et al. The ISPRS benchmark on urban object classification and 3D building reconstruction. ISPRS Anna Photog, Rem Sens Spa Infor Sci. 2012;1(3):293–298.
   Google Scholar
• Kang L, Gellert M. Fast multiclass vehicle detection on aerial images. IEEE Geosci Rem Sens Let. 2015;12(9):1938–1942.
   Web of Science ®Google Scholar
• Mnih V. Machine learning for aerial image labeling. University of Toronto (Canada); 2013.
   Google Scholar
• Kemker R, Salvaggio C, Kanan C. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS J Photog Re Sens Let. 2018;145:60–77.
   Web of Science ®Google Scholar
• Volpi M, Tuia D. Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images. ISPRS J Photog Rem Sens Let. 2018;144:48–60.
   Web of Science ®Google Scholar
• Jinwang W, Wei G, Pan T, et al.: Bottle detection in the wild using low-altitude unmanned aerial vehicles. International Confer. Infor. Fusion (FUSION), Cambridge, UK: IEEE, 2018.
   Google Scholar
• Jiandan Z, Tao L, Guangle Y. Robust vehicle detection in aerial images based on cascaded convolutional neural networks. Sens. 2017;17(12):2720–2737.
   PubMed Web of Science ®Google Scholar
• Ying S, Xinchang Z, Silvano G, et al. Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data. ISPRS J Photog Rem Sens Let. 2018;143:3–14.
   Web of Science ®Google Scholar
• Marmanis D, Wegner JD, et al. Semantic segmentation of aerial images with an ensemble of CNNs. ISPRS Ann photog. Rem Sens Spa Infor Sci. 2016;3:473–480.
   Google Scholar
• Qi C, Lei W, Yifan W, et al. Aerial imagery for roof segmentation: a large-scale dataset towards automatic mapping of buildings. ISPRS J Photog Rem Sens Let. 2019;147:42–55.
   Web of Science ®Google Scholar
• Xiang L, Xiaojing Y, Yi F. Building-a-nets: robust building extraction from high-resolution remote sensing images with adversarial networks. IEEE J Sel Top App Earth Obs Rem Sens Let. 2018;11(10):3680–3687.
   Web of Science ®Google Scholar
• Lichao M, Xiaoxiang Z. Vehicle instance segmentation from aerial image and video using a multi-task learning residual fully convolutional network. IEEE Trans Geosci Rem Sens. 2018;56(11):6699–6711.
   Web of Science ®Google Scholar
• Hani A, Eduard T, et al. Learning to match aerial images with deep attentive architectures. IEEE Conference Computer Vision Pattern Recognition (CVPR), 3539–3547, 2016.
   Google Scholar
• Ruihua W, Xionwu X, Bingxuan G, et al. An effective image denoising method for UAV images via improved generative adversarial networks. Sens. 2018;18(7):1985–2007.
   PubMed Web of Science ®Google Scholar
• Benjamin K, Diego M, Devis T. Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning. Rem Sens Enviro. 2018;216:139–153.
   Web of Science ®Google Scholar
• Yang L, Peng S, Highsmith M R, et al. Performance comparison of deep learning techniques for recognizing birds in aerial images. IEEE Third International Conference on DSC; Guangzhou, China, 317–324, 2018.
   Google Scholar
• Amin F, Mohammad E, Babak M. A deep residual neural network for low altitude remote sensing image classification. Ira. Joi. CFIS, IEEE, Kerman, Iran, 2018.
   Google Scholar
• Wei H, Ruyi F, Lizhe W, et al. A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS J: Photog Rem Sens Let. 2018;145:23–43.
   Web of Science ®Google Scholar
• Otavio AP, Keiller N, Jefersson AdS, et al. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains. IEEE Conference Computer Vision Pattern Recognition (CVPR) Workshops, 44–51, 2015.
   Google Scholar
• Fan H, GuiSong X, Jingwen H, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Rem Sens. 2015;7(11):14680–14707.
   Google Scholar
• Barak O, Annie H, et al. Infrastructure quality assessment in Africa using Satellite Imagery and Deep Learning, arXiv preprint, arXiv:.00894 (2018).
   Google Scholar
• Vysakh SM, Sowmya V, Soman KP. Deep neural networks as feature extractors for classification of vehicles in aerial imagery. International Conference on SPIN, Noida, India, 105–110, 2018.
   Google Scholar
• Makantasis K, Karantzalos K, Anastasios D, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks. IEEE IGARSS; Milan, Italy, 4959–4962, 2015.
   Google Scholar
• Yuhao C, Javier R, Edward JD. Estimating plant centers using a deep binary classifier. IEEE SSIAI, Las Vegas, NV, USA, IEEE, 2018
   Google Scholar
• Rui C, Jiasong Z, Wei T, et al. Integrating aerial and street view images for urban land use classification. Rem Sens. 2018;10(10):1553–1575.
   Google Scholar
• Adriana R, Carlo G, Gustau CV. Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Rem Sens. 2016;54(3):1349–1362.
   Web of Science ®Google Scholar
• Jasper R U, Koen EA, Van DS, et al. Selective search for object recognition. Int J Comp Vis. 2013;104(2):154–171.
   Web of Science ®Google Scholar
• Joseph R, Santosh D, Ross G, et al. You only look once: unified, real-time object detection. Proceedings of IEEE Conference on CVPR, 779–788, 2016.
   Google Scholar
• Shaoqing R, Kaiming H, Ross G, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pat Anal & Mac Int. 2017;39(6):1137–1149.
   PubMed Web of Science ®Google Scholar
• Mahyar N, Pouya S, Rama C, et al. Ssh: single stage headless face detector. IEEE ICCV, 4875–4884, 2017.
   Google Scholar
• Xiaohong W, Abhinav S, and Abhinav G. A-fast-rcnn: hard positive generation via adversary for object detection. Proceedings of IEEE Conference on Computer Vision Pattern Recognition, 2606–2615, 2017.
   Google Scholar