Abstract
Remote sensing image scene interpretation has many applications on land use land covers; thanks to many satellite technologies innovations that generate high-quality images periodically for analysis and interpretation through computer vision techniques. In recent literature, deep learning techniques have demonstrated to be effective in image feature learning thus aiding several computer vision applications on land use land cover. However, most deep learning techniques suffer from problems such as the vanishing gradients, network over-fitting, among other challenges of which the different literature works have attempted to address from varying perspectives. The goal of machine learning in remote sensing is to learn image feature patterns extracted by computer vision techniques for scene classification tasks. Many applications that utilize data from remote sensing are on the surge, this include, aerial surveillance and security, smart farming, among others. These applications require to process satellite image information effectively and reliably for appropriate responses. This research proposes the deep residual feature learning network that is effective in image feature learning which can be utilizable in a networked environment for appropriate decision making processes. The proposed strategy utilizes short-cut connections and mapping functions for deep feature learning. The proposed technique is evaluated on two publicly available remote sensing datasets and it attains superior classification accuracy results of 96.30% and 92.56% respectively on the Ucmerced and Whu-siri datasets, improving the state-of-the-art significantly.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A.: Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J. Photogrammetry Remote Sens. 152, 166–177 (2019)
Bazi, Y., Al Rahhal, M.M., Alhichri, H., Alajlan, N.: Simple yet effective fine-tuning of deep CNNs using an auxiliary classification loss for remote sensing scene classification. Remote Sens. 11(24), 2908 (2019)
Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)
Xia, G.S., et al.: AID: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965–3981 (2017)
Zhou, Q., Zheng, B., Zhu, W., Latecki, L.J.: Multi-scale context for scene labeling via flexible segmentation graph. Pattern Recogn. 59, 312–324 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 16 (2018)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Tombe, R.: Computer vision for smart farming and sustainable agriculture. In: 2020 IST-Africa Conference (IST-Africa), pp. 1–8. IEEE, May 2020
Wójtowicz, M., Wójtowicz, A., Piekarczyk, J.: Application of remote sensing methods in agriculture. Commun. Biometry Crop Sci. 11(1), 31–50 (2016)
Ghassemian, H.: A review of remote sensing image fusion methods. Inf. Fusion 32, 75–89 (2016)
Bu, S., Han, P., Liu, Z., Han, J.: Scene parsing using inference embedded deep networks. Pattern Recogn. 59, 188–198 (2016)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4151–4160 (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Choromanska, A., Henaff, M., Mathieu, M., Arous, G.B., LeCun, Y.: The loss surfaces of multilayer networks. In: Artificial Intelligence and Statistics, pp. 192–204, February 2015
Mishkin, D., Matas, J.: All you need is a good init. arXiv preprint arXiv:1511.06422 (2015)
Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning. pp. 1139–1147, February 2013
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM, November 2014
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence, February 2017
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256, March 2010
Martinez-Covarrubias, J.: Algorithms for large-scale multi-codebook quantization. Doctoral dissertation, University of British Columbia (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Sun, J.: Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5353–5360 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Movshovitz-Attias, Y., Yu, Q., Stumpe, M.C., Shet, V., Arnoud, S., Yatziv, L.: Ontological supervision for fine grained classification of street view storefronts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1693–1702 (2015)
Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)
Arora, S., Bhaskara, A., Ge, R., Ma, T.: Provable bounds for learning some deep representations. In: International Conference on Machine Learning, pp. 584–592, January 2014
Wadhwa, A., Madhow, U.: Bottom-up deep learning using the Hebbian principle (2016)
Jegou, H., Perronnin, F., Douze, M., Sánchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2011)
Liu, Q., Hang, R., Song, H., Zhu, F., Plaza, J., Plaza, A.: Adaptive deep pyramid matching for remote sensing scene classification. arXiv preprint arXiv:1611.03589 (2016)
Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 270–279, November 2010
Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, June 2007
Chatfield, K., Lempitsky, V.S., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: BMVC, vol. 2, no. 4, p. 8, September 2011
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (2007)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, ICML 2010, pp. 807–814 (2010)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Zhao, H.H., Liu, H.: Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granular Comput. 5(3), 411–418 (2020)
Cheriyadat, A.M.: Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 52(1), 439–451 (2013)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, vol. 2, pp. 2169–2178. IEEE, June 2006
Xia, G.S., Yang, W., Delon, J., Gousseau, Y., Sun, H., Maître, H.: Structural high-resolution satellite image indexing, July 2010
Gong, X., Xie, Z., Liu, Y., Shi, X., Zheng, Z.: Deep salient feature based anti-noise transfer network for scene classification of remote sensing imagery. Remote Sens. 10(3), 410 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Tombe, R., Viriri, S. (2021). Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2020. Lecture Notes in Computer Science(), vol 12629. Springer, Cham. https://doi.org/10.1007/978-3-030-70866-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-70866-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70865-8
Online ISBN: 978-3-030-70866-5
eBook Packages: Computer ScienceComputer Science (R0)