Performance of Deep Learning in Land Use Land Cover Classification of Indian Remote Sensing (IRS) LISS – III Multispectral Data
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Abstract
Identification of land use land cover is a very important task. However, methods existing for the above mention purpose are labor incentives, time-consuming, and costly. Remote sensing plays very important role in the mappings. classification of land cover features and offers very noteworthy and sensed information. The present study shows the semantic segmentation of Indian remote sensing (IRS) LISS-III multispectral image and the comparison of three algorithms U-Net, Deeplabv3+and Tiramisu. The deep neural network was used to perform the study. We present total 3 innovative datasets, built on these LISS-III images that has 4 different spectral bands (Band – 2 (Blue), Band-3 (Green), Band-4(Red), and Band-5 (Nearly Infrared), FCC (false color composite) images and the ground truth mask images. Dataset has 13500 labelled images. A fully-convolutional network (FCN) with skip connections is trained to take an input image of size 128 X 128 X 3 and outputs a matrix of shape 128 X 128 X 4 i.e., a one-hot encoded version of the mask. The experiment identifies 4 classes successfully (Water Bodies, Vegetation, Uncultivated Land, and Residential areas). The experiment showed that the U-Net algorithm has a very good capability for the classification of LISS -III images for land use land cover class detection then Tiramisu and Deeplabv3+. U-Net achieved accuracy 84%, Deelabv3+ achieved 29% whereas Tiramisu achieved accuracy 33%.
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Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., & Fulham, M. (2019). Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern recognition, 85, 78-89.
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.
Chen,Y.-C.,Chiu, H.-W., Su,Y.-F.,Wu, Y.-C., andCheng,K.-S. (2017). Does urbanization increase diurnal land surface temperature variation? Evidence and implications. Landscape Urban Plann. 157, 247–258. doi:10.1016/j.landurbplan.2016.06.014
Codts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke,W.; Roth, S.; Schiele, B.The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 3213–3223.
Dian-lai, W., Ai-xia, S., & Wen-ping, L. (2020, November). Application of deep neural networks in classification of medium resolution remote sensing image. In Journal of Physics: Conference Series (Vol. 1682, No. 1, p. 012014). IOP Publishing.
Fan, J., Cao, X., Yap, P. T., & Shen, D. (2019). BIRNet: Brain image registration using dual-supervised fully convolutional networks. Medical image analysis, 54, 193-206.
Fu, G.; Zhao, H.; Li, C.; Shi, L. Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique. Remote Sens. 2013, 5, 3259–3279.
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
Huang, Y., Zhou, F., & Gilles, J. (2019). Empirical curvelet based fully convolutional network for supervised texture image segmentation. Neurocomputing, 349, 31-43.
Hung, W.-C., Chen, Y.-C., and Cheng, K.-S. (2010). Comparing landcover patterns in Tokyo, Kyoto, and Taipei using ALOS multispectral images. Landscape Urban Plann. 97, 132–145. doi:10.1016/j.landurbplan.2010.05.004
Karoui, M. S., Deville, Y., Hosseini, S., Ouamri, A., & Ducrot, D. (2009, August). Improvement of remote sensing multispectral image classification by using independent component analysis. In 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (pp. 1-4). IEEE.
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. In Proceedings of the Neural Information Processing Systems (NIPS) Conference, La Jolla, CA, USA,3–8 December 2012.
Lateef, F., & Ruichek, Y. (2019). Survey on semantic segmentation using deep learning techniques. Neurocomputing, 338, 321-348.
Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
Li, C., Wang, X., Liu, W., Latecki, L. J., Wang, B., & Huang, J. (2019). Weakly supervised mitosis detection in breast histopathology images using concentric loss. Medical image analysis, 53, 165-178.
Li, L., Han, L., Ding, M., Cao, H., & Hu, H. (2021). A deep learning semantic template matching framework for remote sensing image registration. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 205-217.
Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 5–7 June 2015; pp. 3431–3440
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence.
Moreira, R. C. (2008). Estudo espectral de alvos urbanos com imagens do sensor HSS (Hyperspectral Scanner System) (Doctoral dissertation, PhD Thesis, National Institute for Space Research (INPE)).
Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., ... & Schilling, M. (2020). Deep learning for understanding satellite imagery: An experimental survey. Frontiers in Artificial Intelligence, 3, 534696.
Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Sarah C. Goslee" Analyzing Remote Sensing Data in R: The Landsat Package", Journal of Statistical Software, July 20i I, Volume 43, Issue http://www.jstatsoft.orgl
Schowengerdt, R. A. (2006). Remote sensing: models and methods for image processing. Elsevier.
Suneetha, Manne, et al. "Object based Classification of Multispectral Remote Sensing Images for Forestry Applications." Proceedings of the 2020 3rd International Conference on Image and Graphics Processing. 2020.
Teng, S. P., Chen, Y. K., Cheng, K. S., and Lo, H. C. (2008). Hypothesis-test-based landcover change detection using multi-temporal satellite images – A comparative study. Adv. Space Res. 41, 1744–1754. doi:10.1016/j.asr.2007.06.064
Xu, X., Chen, Y., Zhang, J., Chen, Y., Anandhan, P., & Manickam, A. (2021). A novel approach for scene classification from remote sensing images using deep learning methods. European Journal of Remote Sensing, 54(sup2), 383-395.
Yang, C., Luo, J., Hu, C., Tian, L., Li, J., and Wang, K. (2018). An observation task chain representation model for disaster process-oriented remote sensing satellite sensor planning: a flood water monitoring application. Remote Sens. 10, 375. doi:10.3390/rs10030375
Zhang, L.; Zhang, L.; Du, B. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geosci. Remote Sens. Mag. 2016, 4, 22–40.