Abstract
Artificial intelligence is the blessing to modern computer science which deals with making a machine intelligent. Many technologies are rising rapidly with the evolution of AI. Machine learning and deep learning has become two powerful AI technologies, widely used in research field. Deep learning is a revolution in the field of image processing which can became a popular method for research in current decade. Deep learning can be easily implemented on medical image analysis, under water image analysis, remote sensing images for detection and classification. This paper aims to design a modified convolutional neural network model for classification using RSSCN7 image dataset. The result of the classification is shown with a confusion matrix which indicates the classification performance.
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References
Y. Gao, J. Shi, J. Li, R. Wang, Remote sensing scene classification based on high-order graph convolutional network. Eur. J. Remote. Sens. 54(S1), 141–155 (2021)
W. Li, H. Liu, Y. Wang, Z. Li, Y. Jia, G. Gui, Deep learning-based classification methods for remote sensing images in urban built-up areas. IEEE Access 7, 36274–36284 (2019)
M.E. Hodgson, Reducing the computational requirements of the minimum-distance classifier. Remote Sens. Environ. 25(1), 117–128 (1988)
K.-Y. Huang, The use of a newly developed algorithm of divisive hierarchical clustering for remote sensing image analysis. Int. J. Remote Sens. 23(16), 149–168 (2006)
A. Chambolle, An algorithm for total variation minimization and applications. J. Math. Imag. Vis. 20(1), 89–97 (2004)
M. Pal, Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26(1), 217–222 (2007)
G. Cheng, J. Han, X. Lu, Remote sensing image scene classification: Benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)
L. Gómez-Chova, D. Tuia, G. Moser, G. Camps-Valls, Multimodal classification of remote sensing images: a review and future directions. Proc. IEEE 103(9), 1560–1584 (2015)
A. Canetti, M.C. Gárrastazu, P.P. de Mattos, E.M. Braz, S.P. Netto, Understanding multi-temporal urban forest cover using high resolution images. Urban For Urban Greening 29, 106–112 (2018)
A. Milan, An integrated framework for road detection in dense urban area from high-resolution satellite imagery and Lidar data. J. Geograph. Inf. Syst. 10(2), 175–192 (2018)
Y. Wang, A.S. Chen, G. Fu, S. Djordjevi, C. Zhang, D.A. Savić, An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features. Environ. Model. Softw, 107, 85–95 (2018)
S.-C.B. Lo, H.-P. Chan, J.-S. Lin, H. Li, M.T. Freedman, S.K. Mun, Artificial convolution neural network for medical image pattern recognition. Neural Netw. 8(7–8), 1201–1214 (1995)
R. Zand, K.Y. Çamsarı, I. Ahmed, S.D. Pyle, C. Kim, S. Datta, R. Demara, R-DBN: A resistive deep belief network architecture leveraging the intrinsic behavior of probabilistic devices, in Proceeding of the ACM Great Lakes Symposium VLSI (GLSVLSI) (2018), abs/1710.00249, pp. 1–8
M.Y. Miao, M. Gowayyed, EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding, in Proceeding of the Automatic Speech Recognition Understand (2016), pp. 167–174, Dec 2016
Q. Zou, L. Ni, T. Zhang, Q. Wang, Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett 12(11), 2321–2325 (2015)
Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification. in Proceeding of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (2010), pp 270–279
D. Lindenbaum, T. Bacastow, SpaceNet: A remote sensing dataset and challenge series, in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 1–10, Jul 2018
H. Tayara, K.T. Chong, Object detection in very high-resolution aerial images using one-stage densely connected feature pyramid network. Sensors 18(10), 3341, (2018)
G. Cheng, P. Zhou, J. Han, Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405–7415 (2016)
G.J. Scott, M.R. England, W.A. Starms, R.A. Marcum, C.H. Davis, Training deep convolutional neural networks for land-cover classification of high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 14(4), 549–553 (2017)
H. Zhai, H. Zhang, L. Zhang, P. Li, Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery. ISPRS J. Photogramm. Remote Sens. 144, 235–253 (2018)
C. Yang, F. Rottensteiner, C. Heipke, A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases. ISPRS J. Photogramm. Remote. Sens. 177, 38–56 (2021)
S. -C. Hung, H. -C. Wu, M.H. Tseng, Remote sensing scene classification and explanation using RSSCNet and LIME. Appl. Sci. 10, 6151 (2020)
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient based learning applied to documentation recognition, in Proceedings of the IEEE (1998), pp. 2278–2324 Dec 1998
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Sahu, S.R., Panda, S. (2022). A Deep Learning-Based Classifier for Remote Sensing Images. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_24
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DOI: https://doi.org/10.1007/978-981-16-9447-9_24
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