Di Bisceglie M, Galdi C. CFAR detection of extended objects in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005,43(4):833-843.
[2]
Zhao H Y, Wang Q, Huang J J, et al . Method for inshore ship detection based on feature recognition and adaptive background window[J]. Journal of Applied Remote Sensing, 2014,8(1):1-14.
Han J W, Zhou P C, Zhang D W, et al . Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,89(1):37-48.
[8]
Zhang L F, Zhang L P, Tao D C, et al . Sparse Transfer Manifold Embedding for Hyperspectral Target Detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(2):1030-1043.
[9]
Bosch A, Zisserman A, Munoz X. Scene classification using a hybrid generative/discriminative approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(4):712-727.
[10]
Zhao L J, Tang P, Huo L Z. A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification[J]. International Journal of Remote Sensing, 2014,35(6):2296-2310.
[11]
Li J, Liu Z. The Study of Scene Classification in the Multisensor Remote Sensing Image Fusion[J]. Mathematical Problems in Engineering, 2013,2013(2013):1-10.
[12]
Guo L, Chehata N, Mallet C, et al . Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011,66(1):56-66.
[13]
Cheng G, Guo L, Zhao T Y, et al . Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA[J]. International Journal of Remote Sensing, 2013,34(1):45-59.
[14]
Zhao B, Zhong Y F, Zhang L P. Scene classification via latent Dirichlet allocation using a hybrid generative/discriminative strategy for high spatial resolution remote sensing imagery[J]. Remote Sensing Letters, 2013,4(12):1204-1213.
[15]
Gong Y, Wang L, Guo R, et al . Multi-scale orderless pooling of deep convolutional activation features[C]. 13th European Conference on Computer Vision (ECCV), SEP 06-12, 2014:392-407.
[16]
Williams D P, Fakiris E. Exploiting environmental information for improved underwater target classification in Sonar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(10):6284-6297.
Marconcini M, Fernandez-Prieto D. A novel approach to targeted land-cover classification of remote-sensing images[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012:7345-7348.
[19]
He W, Zhou W, Su P. Research on Classification and Target Recognition of Remote Sensing Image Based on Improved Support Vector Machine[J]. Remote Sensing Information, 2010(6):6-8,13.
[20]
Dai D X, Yang W. Satellite Image Classification via two-layer sparse coding with biased image representation [J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(1):173-176.
[21]
Luo L, Hu Y. Explorations on object-oriented classification for ground targets from high-resolution image [C]. International Forum on Information Technology and Applications (IFITA 2009), 2009:139-143.
Blaschke T, Hay G J, Kelly M, et al . Geographic object-based image analysis -towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,87(1):180-191.
Zhang L F, Zhang L P, Tao D C, et al . A multifeature tensor for remote-sensing target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(2):374-378.
[26]
Guo W Y, Xia X Z, Wang X F. A remote sensing ship recognition method based on dynamic probability generative model[J]. Expert Systems with Applications, 2014,41(14):6446-6458.
Lu C Y, Zou H X, Sun H, et al . Combing rough set and RBF neural network for large-scale ship recognition in optical satellite images[J]. 35th International Symposium on Remote Sensing of Environment (ISESE35),2014,17(1):1-6.
[29]
Prasad S, Bruce L M. Decision fusion with confidence-based weight assignment for hyperspectral target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(5):1448-1456.
[30]
Sun J D, Fan G L, Yu L J, et al . Concave-convex local binary features for automatic target recognition in infrared imagery[J]. Eurasip Journal on Image and Video Processing, 2014, 2014(23):1-13.
[31]
Wang M G, Tian Y G. Target recognition of infrared bridge image based on morphological operator[C]. International Conference on Advances in Engineering (ICAE), 2011:490-494.
[32]
Lang H T, Zhang J, Zhang T, et al . Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery[J]. Journal of Applied Remote Sensing, 2014,8(1):1-17.
[33]
Bryner D, Srivastava A. Shadow segmentation in SAS and SAR using bayesian elastic contours[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2013:375-380.
[34]
Lopera O, Dupont Y. Automated target recognition with SAS: Shadow and highlight-based classification [C]. 2012 Oceans, 2012:1-5.
[35]
Isaacs J C, Tucker J D. Signal diffusion features for automatic target recognition in synthetic aperture sonar [C]. Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE, 2011:461-465.
[36]
Fei T, Kraus D, Zoubir A M. Contributions to automatic target recognition systems for underwater mine classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(1):505-518.
[37]
Wang X Y, Li Z H, Gao S. Parallel remote sensing image processing: Taking image classification as an example [C]. 6th International Symposium, ISICA 2012, October 27-28, 2012:159-169.
[38]
Yang J H, Zhang J X, Huang G M. A parallel computing paradigm for pan-sharpening algorithms of remotely sensed images on a multi-core computer[J]. Remote Sensing, 2014,6(7):6039-6063.
[39]
Qu H C, Zhang J P, Chen Y S, et al . Parallel implementation for SAM algorithm based on GPU and distributed computing [C]. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012:4074-4077.
[40]
Yang C T, Huang C L, Lin C F. Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU clusters[J]. Computer Physics Communications, 2011,182(1):266-269.
[41]
Timchenko L, Yarovyy A, Kokriatskaia N, et al . Application of parallel-hierarchical transformations for rapid recognition of dynamic images based on GPU technology[C]. Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013), 2013:224-228.
[42]
Chen H, Liu X Y, Shao S, et al . A GPU-paralleled implementation of an enhanced face recognition algorithm[C]. Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 2013:1-10.
[43]
Li G Q, Liu D S. Key technologies research on building a cluster-based parallel computing system for remote sensing [C]. Computational Science - ICCS 2005, Pt 3, 2005:484-491.
[44]
Jing X Y, Li S, Zhang D, et al . Supervised and unsupervised parallel subspace learning for large-scale image recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012,22(10):1497-1511.
Taylor R C. An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics[C]. Proceedings of the 11th Annual Bioinformatics Open Source Conference (BOSC) 2010, July 2010:1-6.
[47]
Dean J, Ghemawat S. MapReduce: A flexible data processing tool[J]. Communications of the ACM, 2010,53(1):72-77.
[48]
Lv Z H, Hu Y J, Zhong H D, et al . Parallel K-means clustering of remote sensing images based on MapReduce[C]. Web Information Systems and Mining 2010, October 23-24, 2010:162-170.
Lee H, Grosse R, Ranganath R, et al . Unsupervised learning of hierarchical representations with convolutional deep belief networks[J]. Communications of the ACM, 2011,54(10): 95-103.
[53]
Le Q V, Ranzato M A, Monga R, et al . Building high-level features using large scale unsupervised learning [C]. 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013:8595-8598.
[54]
Dahl G E, Yu D, Deng L, et al . Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012,20(1):30-42.
[55]
Masci J, Meier U, Ciresan D, et al . Stacked convolutional auto-encoders for hierarchical feature extraction[C]. Artificial Neural Networks and Machine Learning - ICANN 2011, Pt I, 2011:52-59.
[56]
Smirnov E A, Timoshenko D M, Andrianov S N. Comparison of regularization methods for ImageNet classification with deep convolutional neural networks[C]. 2nd AASRI Conference on Computational Intelligence and Bioinformatics (CIB), 2014:89-94.
[57]
Chen Y S, Lin Z H, Zhao X, et al . Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(6):2094-2107.
[58]
Gondek D C, Lally A, Kalyanpur A, et al . A framework for merging and ranking of answers in DeepQA[J]. IBM Journal of Research and Development, 2012,56(3-4):1-12.
[59]
Ghosh-Dastidar S, Adeli H. Spiking neural networks[J]. International Journal of Neural Systems, 2009,9(4):295-308.
[60]
Liu Y D, Wang L M. Application of memristor-based spiking neural network in image edge extraction[J]. Acta Physica Sinica, 2014,63(8):1-7.
Ogiela L, Ogiela M R, Tadeusiewicz R. Ubiquitous computing in creation of cognitive systems for medical images interpretation[C]. International Conference on Ubiquitous Computing and Multimedia Applications, JUN 23-25, 2010:44-50.
Wang L Z, Lu K, Liu P, et al . IK-SVD: Dictionary learning for spatial big data via incremental atom update[J]. Computing in Science & Engineering, 2014,16(4):41-52.
[68]
Luo W, Li H L, Liu G H. Automatic annotation of multispectral satellite images using author-topic Model[J]. IEEE Geoscience and Remote Sensing Letters, 2012,9(4):634-638.
