Abstract
Image categorization is the process of assigning land cover classes to pixels. It may categorize images into forest, urban, agricultural, and other categories. The approaches in this study are tested using a large image dataset comprising 21 land use categories. There are comparisons to be done in addition to traditional approaches. DWT at two degrees of decomposition is used to extract texture features from remote sensing images. The results are explained using the UC Merced dataset. At the approximation sub-band, 72 features are extracted at the second level of decomposition using DWT and other feature extraction methods. The data is classified using the supervised support vector machine (SVM) approach, and the results with the highest accuracy are determined.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)
Nogueira, K., Penatti, O.A.B., Dos Santos, J.A.: Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognit. 61, 539–556 (2017)
Nijhawan, R., et al.: A futuristic deep learning framework approach for land use-land cover classification using remote sensing imagery. In: Advanced Computing and Communication Technologies, pp. 87–96. Springer, Singapore (2019)
Tong, X.-Y., et al.: Learning transferable deep models for land-use classification with high-resolution remote sensing images. ar**v preprint ar**v:1807.05713 (2018)
Carranza-GarcÃa, M., GarcÃa-Gutiérrez, J., Riquelme, J.C.: A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens. 11(3), 274 (2019)
Xu, L., et al.: Multi-structure joint decision-making approach for land use classification of high-resolution remote sensing images based on CNNs. IEEE Access 8, 42848–42863 (2020)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prabhakar, T., Srujan Raju, K., Reddy Madhavi, K. (2022). Support Vector Machine Classification of Remote Sensing Images with the Wavelet-based Statistical Features. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 2. Smart Innovation, Systems and Technologies, vol 283. Springer, Singapore. https://doi.org/10.1007/978-981-16-9705-0_59
Download citation
DOI: https://doi.org/10.1007/978-981-16-9705-0_59
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9704-3
Online ISBN: 978-981-16-9705-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)