Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images Using Neural Network Method
Keywords:Feature classification, machine learning, neural network, satellite image.
Classifying features from satellite images has been a time-consuming manual process which requires lots of manpower. This work exploits deep convolutional encoder-decoder neural network to develop an algorithm that can automatically classify the extents of the Pararubber tree-growing areas from the LANDSAT-8 images. The ground truth of the areas of the Pararubber tree was manually prepared and was separated into training datasets and the validation datasets. The classification model from this approach obtained using the training datasets was verified with the classification accuracy of70.90%, precision of 67.66%, recall of 80.80%, and F1 score of 73.59%.
Z. Li, and J. Fox, “Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data,” Applied Geography, vol. 32(2), pp. 420–432, 2012.
Z. Li, and J. Fox, “Integrating Mahalanobis typicalities with a neural network for rubber distribution mapping,” Remote Sensing Letters, vol. 2(2), pp. 157–166, 2011.
Z. Li, and J. Fox, “Rubber tree distribution mapping in northeast Thailand,” International Journal of Geosciences, vol. 2(4), pp. 573, 2011.
C. Wasana, “An approach for estimating area of rubber plantation: integrating satellite and physical data over the Northeast Thailand,” Proceedings of the 31st Asian Conference on Remote Sensing Vietnam, Hanoi, Vietnam, 2010.
H. Hu, W. Liu, and M. Cao, “Impact of land use and land cover changes on ecosystem services in Menglun, Xishuangbanna, Southwest China,” Environmental Monitoring and Assessment, vol. 146(1–3), pp. 147–156, 2008.
K. Hurni, “Rubber in Laos: Detection of actual and assessment of potential plantations in Lao PDR using GIS and remote sensing technologies,” Doctoral dissertation, 2008.
H. Li, Y. Ma, T. Aide, and W. Liu, “Past, present and future land-use in Xishuangbanna, China and the implications for carbon dynamics,” Forest Ecology and Management, vol. 255(1), pp. 16–24, 2008.
H. Li, Y. Ma, T. Aide, W. Liu, and M. Cao, “Demand for rubber is causing the loss of high diversity rain forest in SW China,” Plant Conservation and Biodiversity, Springer, Dordrecht, pp. 16–24, 2006.
M. Suratman, V. LeMay, Q. Gary, G. Donald, N. Walsworth, and L. Peter, “Logistic regression modeling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping,” Science Letters, vol. 2(1), pp. 79–85, 2005.
A. Ekadinata, A. Widayati, and G. Vincent, “Rubber agroforest identification using object-based classification in Bungo District, Jambi, Indonesia,” 25th Asian Conference on Remote Sensing, Chiang Mai, Thailand, pp. 22–26, 2004.
M. Suratman, G. Bull, D. Leckie, V. Lemay, P. Marshall, and M. Mispan, “Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data,” International Forestry Review, vol. 6(1), pp. 12, 2004.
S. Baban, and K. Wan Yusof, “Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS,” International Journal of Remote Sensing, vol. 22(10), pp. 1909–1918, 2001.
V. Badrinarayanan, A. Kendall, R. Cipolla, and S. Member, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE transactions on pattern analysis and machine intelligence 39, no. 12, pp. 2481–2495, 2017.
H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” In Proceedings of the IEEE international conference on computer vision, pp. 1520–1528, 2015.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International Journal of Computer Vision, vol. 111, no. 1, pp. 98–136, 2015.
D. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs),” The International Conference on Learning Representations, 2016.