[1] LI Erzhu,XIA Junshi,DU Peijun,et al.Integrating multilayer features of convolutional neural networks for remote sensing scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(10):5653-5665. [2] CHENG Gong,HAN Junwei,LU Xiaoqiang.Remote sensing image scene classification:benchmark and state of the art[EB/OL][2019-08-23].https://www.research gate.net/publication/314152791_Remote_Sensing_Image_Scene_Classification_Benchmark_and_State_of_the_Art. [3] ZOU Qin,NI Lihao,ZHANG Tong,et al.Deep learning based feature selection for remote sensing scene classification[J].IEEE Geoscience and Remote Sensing Letters,2015,12(11):2321-2325. [4] MELGANI F,BRUZZONE L.Classification of hyperspectral remote sensing images with support vector machines[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790. [5] YE Lihua,WANG Lei,SUN Yaxin,et al.Parallel multi-stage features fusion of deep convolutional neural networks for aerial scene classification[J].Remote Sensing Letters,2018,9(3):294-303. [6] CAO Linlin,LI Haitao,HAN Yanshun,et al.Application of convolutional neural network in high-scoring remote sensing image classification[J].Surveying and Mapping Science,2016,41(9):170-175.(in Chinese) 曹林林,李海涛,韩颜顺,等.卷积神经网络在高分遥感影像分类中的应用[J].测绘科学,2016,41(9):170-175. [7] HUANG Hong,HE Kai,ZHENG Xinlei,et al.Spatial-spectral joint feature extraction of hyperspectral images based on deep learning[J].Laser and Optoelectronics Progress,2017,54(10):174-182.(in Chinese) 黄鸿,何凯,郑新磊,等.基于深度学习的高光谱图像空-谱联合特征提取[J].激光与光电子学进展,2017,54(10):174-182. [8] TANG Xiaoqing,LIU Yazhou,CHEN Junlong.Improved classification method of remote sensing image based on sparse representation[J].Computer Engineering,2016,42(3):254-258.(in Chinese) 唐晓晴,刘亚洲,陈骏龙.基于稀疏表示的遥感图像分类方法改进[J].计算机工程,2016,42(3):254-258. [9] LIU Xiaodan,YUE Shuang.Forest vegetation texture segmentation of remote sensing image based on visual attention[J].Computer Engineering,2018,44(4):274-280.(in Chinese) 刘小丹,岳爽.基于视觉注意的遥感图像森林植被纹理分割[J].计算机工程,2018,44(4):274-280. [10] LI Haifeng,PENG Jian,TAO Chao,et al.What do we learn by semantic scene understanding for remote sensing imagery in CNN framework?[EB/OL].[2019-08-23].https://www.researchgate.net/publication/317040292_What_do_We_Learn_by_Semantic_Scene_Understanding_for_Remote_Sensing_imagery_in_CNN_framework. [11] SONG Xibin,DAI Yuchao,QIN Xueying.Deep depth super-resolution:learning depth super-resolution using deep convolutional neural network[C]//Proceedings of ACCV’16.Berlin,Germany:Springer,2017:360-376. [12] TSAMPIKOS K.Depth-adaptive methodologies for 3D image caregorization[EB/OL].[2019-08-23].https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669124. [13] RAGHU M,POOLE B,KLEINBERG J,et al.On the expressive power of deep neural networks[EB/OL].[2019-08-23].https://www.researchgate.net/publication/304018151_On_the_expressive_power_of_deep_neural_networks. [14] XIE S N,GIRSHICK R,DOLLAR P,et al.Aggregated residual transformations for deep neural networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:21-28. [15] LI L L,WANG L G,TEIXEIRA F L,et al.DeepNIS:deep neural network for nonlinear electromagnetic inverse scattering[J].IEEE Transactions on Antennas and Propagation,2019,67(3):1819-1825. [16] LIU Yi.Review of infrared image complexity evaluation method[EB/OL].[2019-08-23].http://en.cnki.com.cn/Article_en/CJFDTotal-HKBQ201403017.htm. [17] GAO Zhenyu,YANG Xiaomei,GONG Jianming,et al.Research on image complexity description methods[EB/OL].[2019-08-23].http://en.cnki.com.cn/Article_en/CJFDTotal-ZGTB201001023.htm. [18] CIOCCA G,CORCHS S,GASPARINI F,et al.Does color influence image complexity perception?[C]//Proceedings of CCIW’15.New York,USA:ACM Press,2015:139-148. [19] SONG Qinghuan,CHEN Zhongbi,SUN Shaohua,et al.A scene recognition method based on image complexity[C]//Proceedings of International Symposium on Advanced Optical Manufacturing and Testing Technologies.Washington D.C.,USA:IEEE Press,2014:20-26. [20] CHEN Yanqin,DUAN Jin,ZHU Yong,et al.Research on the image complexity based on neural network[C]//Proceedings of 2015 International Conference on Machine Learning and Cybernetics.Washington D.C.,USA:IEEE Press,2015:33-38. [21] MURAMATSU C.Overview on subjective similarity of images for content-based medical image retrieval[J].Radiological Physics and Technology,2018,11(2):109-124. [22] SUCIATI N,HERUMURTI D,WIJAYA A Y.Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition[EB/OL].[2019-08-23].https://www.researchgate.net/publication/313471714_Feature_extraction_using_gray-level_co-occurrence_matrix_of_wavelet_coefficients_and_texture_matching_for_batik_motif_recognition. [23] CHEN Yanqin,DUAN Jin,ZHU Yong,et al.Research on the image complexity based on texture features[EB/OL].[2019-08-23].https://www.researchgate.net/publication/282458233_Research_on_the_image_complexity_based_on_texture_features. [24] HUANG G,LIU Z,VAN D M L,et al.Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:51-57. [25] OYAMA T,YAMANAKA T.Fully convolutional DenseNet for saliency-map prediction[C]//Proceedings of 2017 IAPR Asian Conference on Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:25-32. [26] SABOUR S,FROSST N,HINTON G E.Dynamic routing between capsules[EB/OL].[2019-08-23].https://www.researchgate.net/publication/320627198_Dynamic_Routing_Between_Capsules. [27] LI X Y,KIRINGA I,YEAP T,et al.Exploring deep anomaly detection methods based on capsule net[EB/OL].[2019-08-23].https://www.researchgate.net/publication/334478921_Exploring_Deep_Anomaly_Detection_Methods_Based_on_Capsule_Net. [28] HU J,SHEN L,ALBANIE S,et al.Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:42-49. [29] AN Gaoyun,ZHOU Wen,WU Yuxuan,et al.Squeeze-and-excitation on spatial and temporal deep feature space for action recognition[C]//Proceedings of IEEE International Conference on Signal Processing.Washington D.C.,USA:IEEE Press,2018:52-27. [30] LIU Yanfei,ZHONG Yanfei,FEI Feng,et al.Scene semantic classification based on random-scale stretched convolutional neural network for high-spatial resolution remote sensing imagery[C]//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium.Washington D.C.,USA:IEEE Press,2016:23-29. [31] YU Yunlong,LIU Fuxian.A two-stream deep fusion framework for high-resolution aerial scene classification[EB/OL].[2019-08-23].https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5822919/. |