[2] [2] YANG Y, NEWSAM S. Bagofvisualwds Spatial Extensions f Luse Classification[C] Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Infmation Systems. New Yk, NY: Association f Computing Machinery, 2010: 270279.

[3] G S XIA, J HU, F HU et al. AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 3965-3981(2017).

[4] G CHENG, J HAN, X LU. Remote Sensing Image Scene Classification: Benchmark and State of the Art. Proceedings of the IEEE, 105, 1865-1883(2017).

[8] A KRIZHEVSKY, I SUTSKEVER, G E HINTON. Imagenet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90(2017).

[9] [9] HE K, ZHANG X, REN S, et al. Deep Residual Learning f Image Recognition[C]2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). [S.l.]: IEEE, 2016: 770778.

[10] G CHENG, C YANG, X YAO et al. When Deep Learning Meets Metric Learning Remote Sensing Image Scene Classification via Learning Discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing, 56, 2811-2821(2018).

[11] [11] XIA G S, YANG W, DELON J, et al. Structural Highresolution Satellite Image Indexing[C]ISPRS Commission Technical Commission VII Symposium: 100 Years ISPRS — Advancing Remote Sensing Science. Nottingham: Remote Sensing Photogrammetry Society, 2010, 38: 298303.

[12] Q ZOU, L NI, T ZHANG et al. Deep Learning Based Feature Selection for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 12, 2321-2325(2015).

[13] [13] HU J, JIANG T, TONG X, et al. A Benchmark f Scene Classification of High Spatial Resolution Remote Sensing Imagery[C]2015 IEEE International Geoscience Remote Sensing Symposium (IGARSS). [S.l.]: IEEE, 2015: 50035006.

[14] [14] BASU S, GANGULY S, MUKHOPADHYAY S, et al. Deepsat: A Learning Framewk f Satellite Imagery[C]Proceedings of the 23rd SIGSPATIAL International Conference on Advances In Geographic Infmation Systems. Seattle, Washington: ACM, 2015: 110. DOI: 10.11452820783.2820816.

[15] Q ZHU, Y ZHONG, B ZHAO et al. Bag-of-visual-words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery. IEEE Geoscience and Remote Sensing Letters, 13, 747-751(2016).

[16] Z XIAO, Y LONG, D LI et al. High-resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective. Remote Sensing, 9, 725(2017).

[17] H LI, X DOU, C TAO et al. RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data. Sensors, 20, 1594(2020).

[18] A J GALLEGO, A PERTUSA, P GIL. Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks. Remote Sensing, 10, 511(2018).

[19] P HELBER, B BISCHKE, A DENGEL et al. Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 2217-2226(2019).

[20] W ZHOU, S NEWSAM, C LI et al. PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 197-209(2018).

[21] Q WANG, S LIU, J CHANUSSOT et al. Scene Classification with Recurrent Attention of VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 57, 1155-1167(2018).

[22] D HOU, Z MIAO, H XING et al. V-RSIR: An Open Access Web-based Image Annotation Tool for Remote Sensing Image Retrieval. IEEE Access, 7, 83852-83862(2019).

[23] [23] SUMBUL G, FUELAN M, DEMIR B, et al. Bigearth: A LargeScale Benchmark Archive f Remote Sensing Image Understing[C]2019 IEEE International Geoscience Remote Sensing Symposium. Piscataway: IEEE, 2019: 59015904.

[24] D HOU, Z MIAO, H XING et al. Two Novel Benchmark Datasets from ArcGIS and Bing World Imagery for Remote Sensing Image Retrieval. International Journal of Remote Sensing, 42, 240-258(2021).

[25] H LI, H JIANG, X GU et al. CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification. Sensors, 20, 1226(2020).

[26] G MACHADO, E FERREIRA, K NOGUEIRA et al. AiRound and CV-BrCT: Novel Multiview Datasets for Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 488-503(2020).

[27] [27] HUA Y, MOU L, JIN P, et al. MultiScene: A largescale Dataset Benchmark f Multiscene Recognition in single Aerial Images[J]. IEEE Transactions on Geoscience Remote Sensing, 2022, 60: 5610213.

[28] Y HUA, L MOU, J LIN et al. Aerial Scene Understanding in the Wild: Multi-scene Recognition Via Prototype-Based Memory Networks. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 89-102(2021).

[29] X QI, P ZHU, Y WANG et al. MLRSNet: A Multi-Label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 337-350(2020).

[30] Y LONG, G S XIA, S LI et al. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-Aid. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4205-4230(2021).

[31] Z ZHOU, S LI, W WU et al. NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3228-3242(2021).

[32] [32] LIU K, YANG J, LI S. RemoteSensing CrossDomain Scene Classification: A Dataset Benchmark. Remote Sensing[J]. Remote Sensing, 2022, 14(18): 4635.

[33] [33] DENG J, DONG W, SOCHER R, et al. Image: A Largescale Hierarchical Image Database[C]2009 IEEE Conference on Computer Vision Pattern Recognition. [S.l.]: IEEE, 2009: 248255.

[34] M EVERINGHAM, GOOL L VAN, C K I WILLIAMS et al. The Pascal Visual Object Classes (voc) Challenge. International Journal of Computer Vision, 88, 303-338(2010).

[41] M J SWAIN, D H BALLARD. Color Indexing. International Journal of Computer Vision, 7, 11-32(1991).

[42] [42] STRICKER M, ENGO M. Similarity of Col Images[C]Stage Retrieval f Image Video Databases III. [S.l.]: SPIE, 1995, 2420: 381392.

[43] R M HARALICK, K SHANMUGAM, I H DINSTEIN. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 610-621(1973).

[44] T OJALA, M PIETIKAINEN, T MAENPAA. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971-987(2002).

[45] [45] DALAL N, TRIGGS B. Histograms of iented Gradients f Human Detection[C]2005 IEEE Computer Society Conference on Computer Vision Pattern Recognition (CVPR''05). [S.l.]: IEEE, 2005, 1: 886893.

[46] D LOWE. Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision, 60, 91-110(2004).

[47] [47] BAY H, TUYTELAARS T, GOOL L V. Surf: Speeded up Robust Features[COL]Proceedings of the 9th European Conference on Computer Vision Volume Part I. Berlin, Heidelberg: SpringerVerlag, 2006: 404417. https:doi.g10.100711744023_32.

[48] H YU, W YANG, G S XIA et al. A Color-texture-structure Descriptor for High-resolution Satellite Image Classification. Remote Sensing, 8, 259(2016).

[49] B LUO, S JIANG, L ZHANG. Indexing of Remote Sensing Images with Different Resolutions by Multiple Features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 1899-1912(2013).

[50] [50] JOACHIMS T. Text Categization with Suppt Vect Machines: Learning with Many Relevant Features[C]European Conference on Machine Learning. Berlin, Heidelberg: SpringerVerlag, 1998: 137142.

[51] V RISOJEVIĆ, Z BABIĆ. Fusion of Global and Local Descriptors for Remote Sensing Image Classification. IEEE Geoscience and Remote Sensing Letters, 10, 836-840(2012).

[52] J ZOU, W LI, C CHEN et al. Scene Classification Using Local and Global Features with Collaborative Representation Fusion. Information Sciences, 348, 209-226(2016).

[53] L J ZHAO, P TANG, L Z HUO. Land-use Scene Classification Using a Concentric Circle-structured Multiscale Bag-of-visual-words Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 4620-4631(2014).

[54] [54] PENATTI O, NOGUEIRA K, DOS SANTOS J. Do Deep Features Generalize from Everyday Objects to Remote Sensing Aerial Scenes Domains[C]Proceedings of the 2015 IEEE Conference on Computer Vision Pattern Recognition Wkshops (CVPRW). Piscataway: IEEE, 2015: 4451.

[55] F HU, G S XIA, J HU et al. Transferring Deep Convolutional Neural Networks for the Scene Classification of High-resolution Remote Sensing Imagery. Remote Sensing, 7, 14680-14707(2015).

[56] G CHENG, Z LI, X YAO et al. Remote Sensing Image Scene Classification Using Bag of Convolutional Features. IEEE Geoscience and Remote Sensing Letters, 14, 1735-1739(2017).

[57] J WANG, C LUO, H HUANG et al. Transferring Pre-trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network. Remote Sensing, 9, 225(2017).

[58] W ZHANG, P TANG, L ZHAO. Remote Sensing Image Scene Classification Using CNN-CapsNet. Remote Sensing, 11, 494(2019).

[59] [59] SABOUR S, FROSST N, HINTON G E. Dynamic Routing between Capsules[C]Proceedings of the 31st International Conference on Neural Infmation Processing Systems. Red Hook, NY, United States: Curran Associates Inc., 2017: 38593869.

[60] P DU, E LI, J XIA et al. Feature and Model Level Fusion of Pretrained CNN for remote Sensing Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 2600-2611(2018).

[62] R MINETTO, M P SEGUNDO, S SARKAR. Hydra: An Ensemble of Convolutional Neural Networks for Geospatial Land Classification. IEEE Transactions on Geoscience and Remote Sensing, 57, 6530-6541(2019).

[63] X SUN, Q ZHU, Q QIN. A Multi-level Convolution Pyramid Semantic Fusion Framework for High-Resolution Remote Sensing Image Scene Classification and Annotation. IEEE Access, 9, 18195-18208(2021).

[64] K XU, H HUANG, P DENG et al. Two-stream Feature Aggregation Deep Neural Network for Scene Classification of Remote Sensing Images. Information Sciences, 539, 250-268(2020).

[69] S CHAIB, H LIU, Y GU et al. Deep Feature Fusion for VHR Remote Sensing Scene Classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 4775-4784(2017).

[71] C SHI, T WANG, L WANG. Branch Feature Fusion Convolution Network for Remote Sensing Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5194-5210(2020).

[73] Q BI, K QIN, H ZHANG et al. RADC-Net: A Residual Attention Based Convolution Network for Aerial Scene Classification. Neurocomputing, 377, 345-359(2020).

[74] W TONG, W CHEN, W HAN et al. Channel-attention-based DenseNet Network for Remote Sensing Image Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4121-4132(2020).

[75] D ZHANG, N LI, Q YE. Positional Context Aggregation Network for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 17, 943-947(2019).

[76] L BAI, Q LIU, C LI et al. A Lightweight and Multiscale Network for Remote Sensing Image Scene Classification. IEEE Geoscience and Remote Sensing Letters, 19, 8012605(2022).

[77] J JI, T ZHANG, L JIANG et al. Combining Multilevel Features for Remote Sensing Image Scene Classification with Attention Model. IEEE Geoscience and Remote Sensing Letters, 17, 1647-1651(2019).

[78] J SHEN, T ZHANG, Y WANG et al. A Dual-model Architecture with Grouping-attention-fusion for Remote Sensing Scene Classification. Remote Sensing, 13, 433(2021).

[79] M LI, L LEI, X LI et al. An Adaptive Multilayer Feature Fusion Strategy for Remote Sensing Scene Classification. Remote Sensing Letters, 12, 563-572(2021).

[80] X WANG, L DUAN, A SHI et al. Multilevel Feature Fusion Networks with Adaptive Channel Dimensionality Reduction for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 19, 8010205(2022).

[81] M LI, L LEI, Y SUN et al. A Cross-layer Nonlocal Network for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 19, 8012505(2022).

[82] R CAO, L FANG, T LU et al. Self-attention-based Deep Feature Fusion for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 18, 43-47(2020).

[83] D YU, Q XU, H GUO et al. An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification. Sensors, 20, 1999(2020).

[84] E LI, A SAMAT, P DU et al. Improved Bilinear CNN Model for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 19, 8004305(2020).

[85] N HE, L FANG, S LI et al. Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling. IEEE Transactions on Geoscience and Remote Sensing, 56, 6899-6910(2018).

[86] D YU, H GUO, Q XU et al. Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6372-6383(2020).

[88] [88] WANG Q, ZHANG L, WU B, et al. What Deep CNNs Benefit from Global Covariance Pooling: an Optimization Perspective[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition. [S.l.]: IEEE, 2020: 1077110780.

[89] J LIANG, Y DENG, D ZENG. A Deep Neural Network Combined CNN and GCN for Remote Sensing Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4325-4338(2020).

[91] Y GAO, J SHI, J LI et al. Remote Sensing Scene Classification Based on High-order Graph Convolutional Network. European Journal of Remote Sensing, 54, 141-155(2021).

[92] [92] XU K, HUANG H, DENG P, et al. Deep Feature Aggregation Framewk Driven by Graph Convolutional wk f Scene Classification in remote Sensing[J]. IEEE Transactions on Neural wks Learning Systems, 2022, 33(10): 57515765.

[94] T WEI, J WANG, W LIU et al. Marginal Center Loss for Deep Remote Sensing Image Scene Classification. IEEE Geoscience and Remote Sensing Letters, 17, 968-972(2019).

[95] Y BAZI, L BASHMAL, M RAHHAL et al. Vision Transformers for Remote Sensing Image Classification. Remote Sensing, 13, 516(2021).

[96] J ZHANG, H ZHAO, J LI. TRS: Transformers for Remote Sensing Scene Classification. Remote Sensing, 13, 4143(2021).

[97] [97] LV P, WU W, ZHONG Y, et al. SCViT: A Spatialchannel Feature Preserving Vision Transfmer f Remote Sensing Image Scene Classification[JOL]. IEEE Transactions on Geoscience Remote Sensing, 2022, 60: 112[20230831]. https:doi.g10.1109TGRS.2022.3157671.

[98] X HUANG, F LIU, Y CUI et al. Faster and Better: A Lightweight Transformer Network for Remote Sensing Scene Classification. Remote Sensing, 15, 3645(2023).

[99] [99] OLIVA A, TRALBA A. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope[J]. International Journal of Computer Vision, 2001, 42(3): 145175.

[100] [100] LAZEBNIK S, SCH C, PONCE J. Beyond Bags of Features: Spatial Pyra Matching f Recognizing Natural Scene Categies[C]ICT''07: XXVI International Conference on Thermoelectrics June 35, 2007 Shineville Luxury Rest Jeju, Kea. Piscataway: IEEE, 2007: 21692176.

[101] [101] SIMONYAN K, ZISSERMAN A. Very Deep Convolutional wks f LargeScale Image Recognition[JOL]. CR, 2014, abs1409.1556[20230904]. https:api.semanticscholar.gCpusID:14124313.

[102] [102] SZEGEDY C, LIU W, JIA Y, et al. Going Deeper with Convolutions[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 19.