Accuracy Evaluation of Convolutional Neural Network Classification Algorithms for Building Identification in Rural and Urban Areas from Very-High-Resolution Satellite Imagery in Jambi, Indonesia

• Autorzy: Daniel Adi Nugroho , Muhammad Dimyati , Laswanto
• Języki publikacji: EN

Abstrakty

EN

Accurate land cover data are essential to a reliable decision-making process; therefore, researchers have turned to novel land cover classification algorithms employing machine learning on high-resolution satellite imagery to improve classification accuracy. The experiment presented in this paper aims to assess the accuracy performance of three patch-based, convolutional neural network architectures (LeNet, VGGNet, and XCeption) in classifying building footprints in rural and urban areas from satellite imagery data, with conventional, pixel-based classification algorithms as a benchmark. The experiment concluded that the CNN classification algorithms consistently outperformed pixel-based algorithms in the accuracy of the resulting building-footprint classification raster. It was also demonstrated that larger image patch size does not always improve classification accuracy in all CNN architectures. This study also revealed that the XCeption architecture performed best among the three CNN architectures assessed, with a 72-pixel patch size having the best accuracy.(original abstract)

Słowa kluczowe

EN

Algorithms; Rural areas; Urban economy; Neural networks; Geography; Economic geography

PL

Algorytmy; Obszary wiejskie; Gospodarka miasta; Sieci neuronowe; Geografia; Geografia ekonomiczna

• Czasopismo: Bulletin of Geography. Socio-economic Series
• Rocznik: 2022
• Numer: No. 58
• Strony: 141--154

Twórcy

autor

Daniel Adi Nugroho

• Universitas Indonesia, Depok, Indonesia

autor

Muhammad Dimyati

• Universitas Indonesia, Depok, Indonesia

autor

Laswanto

Bibliografia

• Aroma, J. & Raimond, K. (2016). An Overview of Technological Revolution in Satellite Image Analysis. Journal of Engineering Science and Technology Review, 9(4): 1-6.
• Ayala, C., Sesma, R., Aranda, C. & Galar, M. (2021). A deep learning approach to an enhanced building footprint and road detection in high-resolution satellite imagery. Remote Sensing, 13(16): 1-21.
• Bishop, C.M., Bishop, P.N.C.C.M., Hinton, G. & Press, O.U. (1995). Neural Networks for Pattern Recognition. UK: Clarendon Press.
• Chawda, C., Aghav, J. & Udar, S. (2018). Extracting Building Footprints from Satellite Images using Convolutional Neural Networks. 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018, September 2018, 572-577.
• Chollet, F. (2014). Xception: Deep Learning with Depthwise Separable Convolutions. Computer Vision and Pattern Recognition, 1251-1258.
• Chollet, F. (2015). Keras. Available at: https://keras.io/ (Accessed 10 April 2022).
• Digital Globe. (2017). WorldView-4 Data Sheet. Available at: https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/196/DG2017_WorldView-4_DS.pdf (Accessed 10 April 2022).
• Fatima, S.A., Kumar, A. & Raoof, S.S. (2021). Real Time Emotion Detection of Humans Using Mini-Xception Algorithm. IOP Conference Series: Materials Science and Engineering, 1042(1): 012027.
• Fitri, S.H. & Sumunar, D.R.S. (2019). The Direction of Development of Jambi City Based on Flood Disaster Mitigation. IOP Conference Series: Earth and Environmental Science, 271(1).
• Gikunda, P.K. & Jouandeau, N. (2019). State-of-the-Art Convolutional Neural Networks for Smart Farms: A Review. Advances in Intelligent Systems and Computing, 997: 763-775.
• Government of Indonesia (2013). Peraturan Pemerintah Republik Indonesia Nomor 8 Tahun 2013 Tentang Ketelitian Peta Rencana Tata Ruang (Government of Republic Indonesia Regulation Concerning the Map Accuracy for Spatial Planning - in Indonesian), PP No 8 Tahun 2013, 1.
• Hamwood, J., Alonso-Caneiro, D., Read, S.A., Vincent, S.J. & Collins, M.J. (2018). Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. Biomedical Optics Express, 9(7): 3049.
• Hardiani, H. & Lubis, T.A. (2017). Analysis of leading sector of Jambi City. Jurnal Perspektif Pembiayaan Dan Pembangunan Daerah, 5(1): 1-12.
• Hu, Y., Zhang, Q., Zhang, Y. & Yan, H. (2018). A deep convolution neural network method for land cover mapping: A case study of Qinhuangdao, China. Remote Sensing, 10(12): 1-17.
• Kattenborn, T., Eichel, J. & Fassnacht, F.E. (2019). Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Scientific Reports, 9(1): 1-9.
• Kavzoglu, T. (2009). Increasing the accuracy of neural network classification using refined training data. Environmental Modelling and Software, 24(7): 850-858.
• Khan, A., Sohail, A., Zahoora, U. & Qureshi, A.S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8): 5455-5516.
• Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov, A.(2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 14(5): 778-782.
• Längkvist, M., Kiselev, A., Alirezaie, M. & Loutfi, A. (2016). Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sensing, 8(4): 329.
• Lecun, Y., Bottou, L., Bengio, Y. & Ha, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, November, 1-46.
• Li, L., Qiang, Y., Zheng, Z. & Zhang, J. (2019). Research on the Relationship between the Spatial Resolution and the Map Scale in the Satellite Remote Sensing Cartographies. International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019), 194-199.
• Luo, L., Li, P. & Yan, X. (2021). Deep learning-based building extraction from remote sensing images: A comprehensive review. Energies, 14(23): 1-25.
• Maxar. (2020). 2019 Annual Report. In 2019 Annual Report (Issue January: 26). Available at: https://s22.q4cdn.com/683266634/files/doc_financials/2019/ar/Maxar-2019-AR-Web-PDF.pdf (Accessed 10 April 2022).
• Maxwell, A.E., Warner, T.A. & Guillén, L.A. (2021). Accuracy assessment in convolutional neural network-based deep learning remote sensing studies-Part 1: Literature Review. Remote Sensing, 13(13): 2450.
• Millard, K. & Richardson, M. (2015). On the importance of training data sample selection in Random Forest image classification: A case study in peatland ecosystem mapping. Remote Sensing, 7(7): 8489-8515.
• Muhammad, U., Wang, W., Chattha, S.P. & Ali, S. (2018). Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification. Proceedings - International Conference on Pattern Recognition, 2018-Augus(August), 1622-1627.Pacheco, A.D.P., Junior, J.A.D.S., Ruiz-Armenteros, A.M. & Henriques, R.F.F. (2021). Assessment of k-nearest neighbor and random forest classifiers for mapping forest fire areas in central portugal using landsat-8, sentinel-2, and terra imagery. Remote Sensing, 13(7): 1-25.
• Pal, M., Akshay, Rohilla, H. & Teja, B.C. (2020). Patch Based Classification of Remote Sensing Data: A Comparison of 2D-CNN, SVM and NN Classifiers. CoRR, abs/2006.1. Available at: https://arxiv.org/abs/2006.11767 (Accessed at: 10 April 2022).
• Pan, Z., Xu, J., Guo, Y., Hu, Y. & Wang, G. (2020). Deep learning segmentation and classification for urban village using a worldview satellite image based on U-net. Remote Sensing, 12(10): 1-18.
• Rwanga, S.S. & Ndambuki, J.M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 08(04): 611-622.
• Shah, T.N., Khan, M.Z., Ali, M., Khan, B. & Muhammad, H. (2018). Critical Analysis of Six Frequently Used Classification Algorithms. University of Swabi Journal, 2(2): 36-40.
• Simonyan, K. & Zisserman, A. (2018). Very Deep Convolutional Networks For Large-Scale Image Recognition. American Journal of Health-System Pharmacy, 75(6): 398-406.
• Sultana, F., Sufian, A. & Dutta, P. (2018). Advancements in image classification using convolutional neural network. Proceedings - 2018 4th IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2018, 122-129.
• Tarabalka, Y., Moser, G., Giorgi, A.D.E., Fang, L., Chen, Y., Chi, M., Serpico, S.B. & Benediktsson, J.Ó.N.A. (2018). New Frontiers in Spectral- Spatial Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, September 2018.
• Thanh Noi, P. & Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors (Basel, Switzerland), 18(1).
• The HDF Group. (2019). HDF5 User's Guide. Available at: https://support.hdfgroup.org/.
• Tobler, W. (1987). Measuring Spatial Resolution. Beijing Conference on Land Use and Remote Sensing, July, 12-16. Available at: https://www.researchgate.net/publication/.
• Van Beers, F., Lindström, A., Okafor, E. & Wiering, M.A. (2019). Deep neural networks with intersection over union loss for binary image segmentation. ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 438-445.
• Xiao, J., Wang, J., Cao, S. & Li, B. (2020). Application of a Novel and Improved VGG-19 Network in the Detection of Workers Wearing Masks. Journal of Physics: Conference Series, 1518(1).
• Zhang, M., Lin, H., Wang, G., Sun, H. & Fu, J. (2018). Mapping paddy rice using a Convolutional Neural Network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sensing, 10(11): 1840.HDF5/doc/UG/HDF5_Users_Guide-Responsive HTML5/index.html (Accessed 10 April 2022).291877360_Measuring_spatial_resolution (Accessed 10 April 2022).

Identyfikatory

DOI

10.12775/bgss-2022-0039