In recent years, with the rapid development of remote sensing satellite technology, the resolution of images generated by remote sensing satellites has been increasing. Today, high-resolution remote sensing images (referred to as high-resolution remote sensing images) have become the focus of research in the field of remote sensing. The feature information of high-resolution remote sensing images is richer and more accurate than the medium-low resolution remote sensing image information, and it has more practical significance in practical applications: remote sensing application workers and research scholars perform feature extraction and scenes on high-resolution remote sensing images. Analysis and research on classification, feature recognition, and target detection to meet the actual needs of high-resolution remote sensing images in the military and economic fields as well as in the civil and civilian fields. Among them, feature extraction is the basis for understanding and analyzing high-resolution remote sensing images. Through various feature extraction techniques, the information of the essential attributes in the high-resolution remote sensing images is extracted as features, and the features are used to describe the high-resolution remote sensing images, which facilitates accurate and reasonable understanding and analysis of high-resolution remote sensing images, and avoids redundancy. The negative impacts of information, noise, etc. in practical applications, and thus more efficient and accurate completion of the classification, identification and detection of high-resolution remote sensing images. This paper mainly discusses the research progress of deep network model of high-resolution remote sensing image based on the deep network model of multimedia-based Convolutional Neural Network (CNN), and through the experimental results of high-resolution remote sensing image specific problems, Performance comparison and evaluation based on features extracted by the CNN-based deep network model. Finally, the problems of CNS-based deep network model in the extraction of deep semantic features in high-resolution remote sensing images and future development trends are analyzed, which provides reference for the feature classification tasks of high-resolution remote sensing images.

Nasser Jaber. Remote Sensing Image Classification Model Based on Multimedia Network and Its Knowledge Integration Method. International Journal of Educational Innovation and Science (2022), Vol. 3, Issue 4: 26-41. https://doi.org/10.38007/IJEIS.2022.030403.

[1] Romero A, Gatta C, Camps-Valls G. Unsupervised Deep Feature Extraction for Remote Sensing Image Classification. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(3):1349-1362. DOI:10.1109/TGRS.2015.2478379

[2] Maggiori E, Tarabalka Y, Charpiat G, et al. Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. IEEE Transactions on Geoscience & Remote Sensing, 2016, 55(2):645-657. DOI:10.1109/TGRS.2016.2612821

[3] Min H, Liu B. Ensemble of extreme learning machine for remote sensing image classification. Neurocomputing, 2015, 149(PA):65-70. DOI:10.1016/j.neucom.2013.09.070

[4] Liu D, Ling H, Han X. High Spatial Resolution Remote Sensing Image Classification Based on Deep Learning. Acta Optica Sinica, 2016, 36(4):0428001. DOI:10.3788/AOS201636.0428001

[5] Shi Q, Du B, Zhang L. Spatial coherence-based batch-mode active learning for remote sensing image classification.. IEEE Transactions on Image Processing, 2015, 24(7):2037-2050. DOI:10.1109/TIP.2015.2405335

[6] Li W, Fu H, Le Y, et al. Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. International Journal of Remote Sensing, 2016, 37(23):15.DOI:10.1080/01431161.2016.1246775

[7] Wang L, Zhang J, Peng L, et al. Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Computing, 2017, 21(1):213-221. DOI:10.1007/s00500-016-2246-3

[8] Yue H, Chen W, Wu X, et al. Visualizing bag-of-words for high-resolution remote sensing image classification. Journal of Applied Remote Sensing, 2016, 10(1):015022. DOI:10.1117/1.JRS.10.015022

[9] Li J, Qian D, Li Y. An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Computing, 2015, 20(12):1-7. DOI:10.1007/s00500-015-1739-9

[10] Wan L, Tang K, Li M, et al. Collaborative Active and Semisupervised Learning for Hyperspectral Remote Sensing Image Classification. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(5):2384-2396. DOI:10.1109/TGRS.2014.2359933

[11] Matasci G, Volpi M, Kanevski M, et al. Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(7):3550-3564. DOI:10.1109/TGRS.2014.2377785

[12] Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw, 2015, 61:85-117. https://doi.org/10.1016/j.neunet.2014.09.003

[13] Chan T H, Jia K, Gao S, et al. PCANet: A Simple Deep Learning Baseline for Image Classification?. IEEE Transactions on Image Processing, 2015, 24(12):5017-5032.DOI:10.1109/TIP.2015.2475625

[14] Alipanahi B, Delong A, Weirauch M T, et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology, 2015, 33(8):831. DOI:10.1038/nbt.3300

[15] Chen Y, Lin Z, Xing Z, et al. Deep Learning-Based Classification of Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, 7(6):2094-2107. DOI:10.1109/JSTARS.2014.2329330

[16] Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Medical Image Analysis, 2017, 42(9):60-88. DOI:10.1016/j.media.2017.07.005

[17] Lv Y, Duan Y, Kang W, et al. Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):865-873. DOI:10.1109/TITS.2014.2345663

[18] Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Jama, 2016, 316(22):2402. DOI:10.1001/jama.2016.17216

[19] Herstad S J, Sandven T, Ebersberger B. Recruitment, knowledge integration and modes of innovation. Research Policy, 2015, 44(1):138-153. DOI:10.1016/j.respol.2014.06.007

[20] Revilla E, Knoppen D. Building knowledge integration in buyer-supplier relationships. International Journal of Operations & Production Management, 2015, 35(10):1408-1436. DOI:10.1108/IJOPM-01-2014-0030

[21] Papanikolaou N, Pavlopoulos G A, Pafilis E, et al. BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery. Bioinformatics, 2015, 31(6):3249-3256. DOI:10.1093/bioinformatics/btu524

[22] Cao X, Guo X, Liu H, et al. The role of social media in supporting knowledge integration: A social capital analysis. Information Systems Frontiers, 2015, 17(2):351-362. DOI:10.1007/s10796-013-9473-2

[23] Sankowska A, Söderlund J. Trust, reflexivity and knowledge integration: Toward a conceptual framework concerning mobile engineers. Human Relations, 2015, 68(6):973-1000. DOI:10.1177/0018726714549646

[24] Butler J R A. The challenge of knowledge integration in the adaptive co‐management of conflicting ecosystem services provided by seals and salmon. Animal Conservation, 2015, 14(6):599-601. DOI:10.1111/j.1469-1795.2011.00509.x

[25] Schwendimann B A, Linn M C. Comparing two forms of concept map critique activities to facilitate knowledge integration processes in evolution education. Journal of Research in Science Teaching, 2016, 53(1):70-94. DOI:10.1002/tea.21244

[26] Ou L L, Ryoo K, Linn M C, et al. Measuring Knowledge Integration Learning of Energy Topics: A two-year longitudinal study. International Journal of Science Education, 2015, 37(7):1044-1066. DOI:10.1080/09500693.2015.1016470

[27] Tsai K H, Liao Y C, Hsu T T. Does the use of knowledge integration mechanisms enhance product innovativeness? ☆. Industrial Marketing Management, 2015, 46:214-223. DOI:10.1016/j.indmarman.2015.02.030