Remote Sensing Image Scene Classification

Full Text (PDF, 424KB), PP.13-20

Views: 0 Downloads: 0

Author(s)

Md. Arafat Hussain ,* Emon Kumar Dey

1. Institute of Infromation Technology, University of Dhaka, Dhaka-1000, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2018.04.02

Received: 11 Mar. 2018 / Revised: 28 Apr. 2018 / Accepted: 6 Jun. 2018 / Published: 8 Jul. 2018

Index Terms

Convolutional Neural Network, Remote Sensing Image, Scene Classification, CNN

Abstract

Remote sensing image scene classification has gained remarkable attention because of its versatile use in different applications like geospatial object detection, natural hazards detection, geographic image retrieval, environment monitoring and etc. We have used the strength of convolutional neural network in scene image classification and proposed a new CNN to classify the images. Pre-trained VGG16 and ResNet50 are used to reduce overfitting and the training time in this paper. We have experimented on a recently proposed NWPU-RESISC45 dataset which is the largest dataset of remote sensing scene images. This paper found a significant improvement of accuracy by applying the proposed CNN and also the approaches have applied.

Cite This Paper

Md. Arafat Hussain, Emon Kumar Dey,"Remote Sensing Image Scene Classification", International Journal of Engineering and Manufacturing(IJEM), Vol.8, No.4, pp.13-20, 2018. DOI: 10.5815/ijem.2018.04.02

Reference

[1]Guo, Zhenhua, Lei Zhang, and David Zhang. "A completed modeling of local binary pattern operator for texture classification." IEEE Transactions on Image Processing 19.6 (2010): 1657-1663.

[2]Cheng, Gong, Junwei Han, and Xiaoqiang Lu. "Remote sensing image scene classification: benchmark and state of the art." Proceedings of the IEEE 105.10 (2017): 1865-1883.

[3]Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).

[4]Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010, June). Sun database: Large-scale scene recognition from abbey to zoo. In Computer vision and pattern recognition (CVPR), 2010 IEEE conference on (pp. 3485-3492). IEEE.

[5]Lienou, Marie, Henri Maitre, and Mihai Datcu. "Semantic annotation of satellite images using latent Dirichlet allocation." IEEE Geoscience and Remote Sensing Letters 7.1 (2010): 28-32.

[6]Vatsavai, Ranga Raju, Anil Cheriyadat, and Shaun Gleason. "Unsupervised semantic labeling framework for identification of complex facilities in high-resolution remote sensing images." Data Mining Workshops (ICDMW), 2010 IEEE International Conference on. IEEE, 2010.

[7]A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems (NIPS), 2012, pp. 1097-1105.

[8]K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (9) (2015) 1904-1916.

[9]C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

[10]F. P. S. Luus, B. P. Salmon, F. van den Bergh, B. T. J. Maharaj, Multiview deep learning for land-use classification, IEEE Geoscience and Remote Sensing Letters 12 (12) (2015) 2448-2452.

[11]B. Zhao, B. Huang, Y. Zhong, Transfer learning with fully pretrained deep convolution networks for land-use classification, IEEE Geoscience and Remote Sensing Letters 14 (9) (2017) 1436-1440.

[12]F. Hu, G.-S. Xia, J. Hu, L. Zhang, Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery, Remote Sensing 7 (11) (2015) 14680-14707.

[13]Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Ca_e: Convolutional architecture for fast feature embedding, in: ACM International Conference on Multimedia, 2014, pp. 675-678.

[14]Castelluccio, Marco, et al. "Land use classification in remote sensing images by convolutional neural networks." arXiv preprint arXiv:1508.00092 (2015)

[15]K. Nogueira, O. A. Penatti, J. A. dos Santos, Towards better exploiting convolutional neural networks for remote sensing scene classi_cation, Pattern Recognition 61 (2017) 539 -556.

[16]G. Cheng, J. Han, X. Lu, Remote sensing image scene classi_cation: Benchmark and state of the art, Proceedings of the IEEE 105 (10) (2017) 1865-1883.

[17]Wang, Limin, et al. "Places205-vggnet models for scene recognition." arXiv preprint arXiv:1508.01667 (2015).

[18]Rahman, Md Mostafijur, et al. "Noise adaptive binary pattern for face image analysis." Computer and Information Technology (ICCIT), 2015 18th International Conference On. IEEE, 2015.

[19]Wu, J.; Rehg, J.M. CENTRIST: A visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 1489–1501

[20]Zhou, W., Newsam, S., Li, C., & Shao, Z. (2017). Patternnet: a benchmark dataset for performance evaluation of remote sensing image retrieval. arXiv preprint arXiv:1706.03424.

[21]Dey, E. K., Tawhid, M. N. A., & Shoyaib, M. (2015). An automated system for garment texture design class identification. Computers, 4(3), 265-282.

[22]Islam, S. S., Rahman, S., Rahman, M. M., Dey, E. K., & Shoyaib, M. (2016, May). Application of deep learning to computer vision: A comprehensive study. In Informatics, Electronics and Vision (ICIEV), 2016 5th International Conference on (pp. 592-597). IEEE.

[23]Islam, S. S., Dey, E. K., Tawhid, M. N. A., & Hossain, B. M. (2017). A CNN Based Approach for Garments Texture Design Classification. Advances in Technology Innovation, 2(4), 119-125.

[24]Rahman, M. M., Rahman, S., Dey, E. K., & Shoyaib, M. (2015). A gender recognition approach with an embedded preprocessing. International Journal of Information Technology and Computer Science (IJITCS), 7(7), 19.