Journal of Mining and Environment

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Journal Forms

Journal Metrics

A Deep Neural Network for Classification of Land Use Satellite Datasets in Mining Environments

Document Type : Original Research Paper

Authors

School of Computer Science and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

Abstract

Land use (LU) is one of the most imperative pieces of cartographic information used for monitoring the mining environment. The extraction of land use data sets from remotely sensed satellite images has garnered significant interest in the mining region community. However, classification of LUs from satellite images remains a tedious task due to the lack of availability of efficient coal mining related datasets. Deep learning methods provide great leverage to extract meaningful information from high-resolution satellite images. Moreover, the performance of a deep learning classification approach significantly depends on the quality of the datasets. The present work attempts to demonstrate the generation of satellite-based datasets for the performance analysis of different deep neural network (DNN)-based learning algorithms in the LU classifications of mining regions. The mining regions are broadly classified into distinct regions based on visual inspection, namely barren land, built-up areas, waterbody, vegetation, and active coal mines. In our experimental work, a patch of 100 spatial samples for each of the five features is generated on three scales, as [1 × 1 × 3], [5 × 5 × 3], and [10 × 10 × 3]. Moreover, the effects of different scalabilities of the dataset on classification performances are also analyzed. Furthermore, this case study is implemented for the large-scale benchmark of satellite image datasets for mining regions. In the future, this work can be used to classify LU in the relevant study regions in real time.

Keywords

References
[1]. Balha, A., Mallick, J., Pandey, S., Gupta, S., and Singh, C.K. (2021). A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for LULC mapping. Earth Science Informatics, 14 (4): 2231-2247.
[2]. Battiti, R. (1992). First-and second-order methods for learning: between steepest descent and Newton's method. Neural computation, 4 (2): 141-166.
[3]. Bottou, L. (2003). Stochastic learning. In Summer School on Machine Learning (pp. 146-168). Springer, Berlin, Heidelberg.
[4]. Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv preprint arXiv:1508.00092.
[5]. Gill, P.E., Murray, W., and Wright, M.H. (2019). Practical optimization. Society for Industrial and Applied Mathematics.
[6]. Hagan, M.T., and Menhaj, M.B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE transactions on Neural Networks, 5(6): 989-993.
[7]. Hay, G. J., and Castilla, G. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In Object-based image analysis (pp. 75-89). Springer, Berlin, Heidelberg.
[8]. Jin, C., Netrapalli, P., and Jordan, M.I. (2018). Accelerated gradient descent escapes saddle points faster than gradient descent. In Conference On Learning Theory (pp. 1042-1085). PMLR.
[9]. Johnson, R.A., and Wichern, D.W. (2020). Applied multivariate statistical analysis.
[10]. Kumar, A., and Gorai, A.K. (2022). Application of transfer learning of deep CNN model for classification of time-series satellite images to assess the long-term impacts of coal mining activities on land-use patterns. Geocarto International, 1-21.
[11]. Laban, N., Abdellatif, B., Ebied, H. M., Shedeed, H.A., and Tolba, M.F. (2017). Performance enhancement of satellite image classification using a convolutional neural network. In International Conference on Advanced Intelligent Systems and Informatics (pp. 673-682). Springer, Cham.
[12]. Labat, C., and Idier, J. (2006). Preconditioned conjugate gradient without linesearch: a comparison with the half-quadratic approach for edge-preserving image restoration. In Computational Imaging IV (Vol. 6065, pp. 134-143). SPIE.
[13]. Lee, H., and Kwon, H. (2016). Contextual deep CNN based hyperspectral classification. In 2016 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 3322-3325). IEEE.
[14]. Lemaréchal, C. (2012). Cauchy and the gradient method. Doc Math Extra, 251(254). 10.
[15]. MacKay, D.J. (1992). Bayesian interpolation. Neural computation, 4(3): 415-447.
[16]. Møller, M.F. (1990). A scaled conjugate gradient algorithm for fast supervised learning. Aarhus University. Computer Science Department, 339.
[17]. Pesaresi, M., Huadong, G., Blaes, X., Ehrlich, D., Ferri, S., Gueguen, L., and Zanchetta, L. (2013). A global human settlement layer from optical HR/VHR RS data: Concept and first results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(5): 2102-2131.
[18]. Powell, M.J.D. (1977). Restart procedures for the conjugate gradient method. Mathematical programming, 12(1): 241-254.
[19]. Qu, L. A., Chen, Z., Li, M., Zhi, J., and Wang, H. (2021). Accuracy improvements to pixel-based and object-based lulc classification with auxiliary datasets from Google Earth engine. Remote Sensing, 13(3): 453.
[20]. Riedmiller, M., and Braun, H. (1993). A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In IEEE international conference on neural networks (pp. 586-591).
[21]. Simon, C., and De Vleeschouwer, C. (2021). Intraclass clustering: an implicit learning ability that regularizes DNNs. arXiv preprint arXiv:2103.06733.
[22]. Trujillo-Jiménez, M.A., Liberoff, A.L., Pessacg, N., Pacheco, C., Díaz, L., and Flaherty, S. (2022). SatRed: New classification land use/land cover model based on multi-spectral satellite images and neural networks applied to a semiarid valley of Patagonia. Remote Sensing Applications: Society and Environment, 26, 100703.
[23]. Verma, D., and Jana, A. (2019). LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: Evidence from Mumbai. arXiv preprint arXiv:1909.09774.
[24]. Xia, G. S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., and Lu, X. (2017). AID: A benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3965-3981.
[25]. Xia, G.S., Yang, W., Delon, J., Gousseau, Y., Sun, H., and Maître, H. (2010). Structural high-resolution satellite image indexing. In ISPRS TC VII Symposium-100 Years ISPRS,Vol. 38, pp. 298-303.
[26]. Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., and Atkinson, P.M. (2018). An object-based convolutional neural network (OCNN) for urban land use classification. Remote sensing of environment, 216, 57-70.
Journal of Mining and Environment
Volume 13, Issue 3
July 2022
Pages 797-808
Files
Share
How to cite
Statistics