Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture
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Abstract
This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.
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How to Cite
[1]
N. Pratiwi, Y. N. Fu’adah, and E. Edwar, “Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture”, INFOTEL, vol. 13, no. 2, pp. 54-62, May 2021.
Section
Informatics
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[4] S. Wegscheider et al., “Current achievements to reduce deforestation in Kalimantan,” Indonesian Journal of Geography, vol. 50, no. 2, pp. 109–120, Dec. 2019.
[5] P. V. G. Reddy and L. R. Parvathy, “Satellite Image Classification for Environmental Analysis using Image Processing,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 3, pp. 1513–1517, Jan. 2020.
[6] M. S. Devi and S. Chib, “Classification of Satellite Images Using Perceptron Neural Network,” International Journal of Computational Intelligence Research, vol. 15, no. 1, pp. 1–10, 2019.
[7] V. A. Fedoseev, “Hyperspectral satellite image classification using small training data from its samples,” Journal of Physics: Conference Series, vol. 1096, no. 1, April. 2018.
[8] A. H. T. Al-Ghrairi, Z. H. Abed, F. H. Fadhil, and F. K. Naser, “Classification of Satellite Images Based on Color Features Using Remote Sensing,” International Journal of Computer, vol. 31, no. 1, pp. 42–52, Sept. 2018.
[9] A. K. Rai, N. Mandal, A. Singh, and K. K. Singh, “Landsat 8 OLI Satellite Image Classification using Convolutional Neural Network,” Procedia Computer Science, vol. 167, pp. 987–993, 2020.
[10] M. A. Kadhim and M. H. Abed, "Convolutional neural network for satellite image classification", in Studies in Computational Intelligence, vol. 830, Springer International Publishing, 2020, pp. 165-178.
[11] S. Watanabe, K. Sumi, and T. Ise, “Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests,” BMC Ecology, vol. 20, no. 1, pp. 1–14, Nov. 2020.
[12] E. Miranda, A. B. Mutiara, Ernastuti, and W. C. Wibowo, “Forest classification method based on convolutional neural networks and sentinel-2 satellite imagery,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 19, no. 4, pp. 272–282, Dec. 2019.
[13] P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp. 2217–2226, Feb. 2019.
[14] M. Xin and Y. Wang, “Research on image classification model based on deep convolution neural network,” Eurasip Journal on Image and Video Processing, vol. 2019, no. 1, Feb. 2019.
[15] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in ACM International Conference Proceeding Series, 2012, pp. 145–151.
[16] M. Z. Alom et al., “The history began from AlexNet: A comprehensive survey on deep learning approaches,” arXiv, 2018.
[17] C. Manliguez, “Generalized Confusion Matrix for Multiple Classes,” no. November, pp. 2–4, 2016.
[18] M. Hashemi, “Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation,” Journal of Big Data, vol. 6, no. 1, Nov. 2019.
[19] S. P. Kannojia and G. Jaiswal, “Effects of Varying Resolution on Performance of CNN based Image Classification An Experimental Study,” International Journal of Computer Sciences and Engineering, vol. 6, no. 9, pp. 451–456, Sept. 2018.
[20] S. Afaq and S. Rao, “Significance Of Epochs On Training A Neural Network,” International Journal of Scientific and Technology Research, vol. 19, no. 6, pp. 485–488, June. 2020.
[21] I. Kandel and M. Castelli, “The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset,” ICT Express, vol. 6, no. 4, pp. 312–315, Dec. 2020.
[22] Y. Wu et al., “Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks,” in 2019 IEEE International Conference on Big Data, Big Data 2019, 2019, pp. 1971–1980.