Water Tanks and Swimming Pools Detection in Satellite Images: Exploiting Shallow and Deep-Based Strategies
Resumo
This paper aims to study and to evaluate two distinct approaches for detecting water tanks and swimming pools in satellite images, which can be useful to monitor waterrelated diseases. The first approach, shallow, consists of using a Support Vector Machine in order to classify into positive and negative a discretized color histogram of a given segment of the original image. The second method employs the Faster R-CNN framework for detecting those objects. We built up swimming pools and water tanks datasets over the city of Belo Horizonte to support our experimental analysis. Our results show that the deep learning method greatly outperforms the shallow strategy, achieving an average precision at 0.5 IoU of over 93% on the swimming pool detection task, and over 73% on the water tank one. All the code and datasets are publicly available.
Palavras-chave:
Remote Sensing, Swimming Pool, Water Tank, Detection, Deep-Learning, SVM
Referências
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M. A.Tolle, “Mosquito-borne diseases,” Current Problems in Pediatric and Adolescent Health Care, vol. 39, pp. 97–140, 2009.
F. Chiaravalloti Neto and H. A. d. S. L. Pereira, “Aedes aegypti na região de são josé do rio preto, estado de são paulo,” Master’s thesis, Universidade de São Paulo, 1993.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
C. Cortes and V. Vapnik, “Support-vector networks,” V. Machine Learning, pp. 273–297, 1995.
D. Tien, T. Rudra, and A. Hope, “Swimming pool identification from digital sensor imagery using svm,” 01 2008, pp. 523–527.
S. K. McFEETERS, “The use of the normalized difference water index (ndwi) in the delineation of open water features,” International Journal of Remote Sensing, vol. 17, no. 7, pp. 1425–1432, 1996.
M. Kim, J. Holt, R. Eisen, K. Padgett, W. Reisen, and J. Croft, “Detection of swimming pools by geographic object-based image analysis to support west nile virus control efforts,” Photogrammetric Engineering and Remote Sensing, vol. 77, pp. 1169–1179, 11 2011.
J. A. Saghri and D. A. Cary, “A rectangular-fit classifier for synthetic aperture radar automatic target recognition,” in Applications of Digital Image Processing XXX, A. G. Tescher, Ed., vol. 6696, International Society for Optics and Photonics. SPIE, 2007, pp. 511 – 521.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,” Chemometrics and intelligent laboratory systems, vol. 2, no. 1-3, pp. 37–52, 1987.
M. Alonso and B. Rodríguez-Cuenca, “Semi-automatic detection of swimming pools from aerial high-resolution images and lidar data,” Remote Sensing, vol. 6, pp. 2628–2646, 04 2014.
K. Pasupa and W. Sunhem, “A comparison between shallow and deep architecture classifiers on small dataset,” 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), 2016.
A. Koutsoukas, K. J. Monaghan, X. Li, and J. Huan, “Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data,” Journal of Cheminformatics, vol. 9, no. 1, 2017.
A. McCallum, K. Nigam et al., “A comparison of event models for naive bayes text classification,” in AAAI-98 workshop on learning for text categorization, vol. 752, no. 1. Citeseer, 1998, pp. 41–48.
N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” The American Statistician, vol. 46, no. 3, pp. 175–185, 1992.
V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston, “Random forest: a classification and regression tool for compound classification and qsar modeling,” Journal of chemical information and computer sciences, vol. 43, no. 6, pp. 1947–1958, 2003.
T. Liu, A. Abd-Elrahman, J. Morton, and V. L. Wilhelm, “Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system,” GIScience & Remote Sensing, vol. 55, no. 2, pp. 243–264, 2018.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “Slic superpixels,” Technical report, EPFL, 06 2010.
S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and the scikit-image contributors, “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, 6 2014.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” 2015.
R. Girshick, “Fast r-cnn,” 2015.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” 2018.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision. Springer, 2014, pp. 740–755.
Publicado
07/10/2020
Como Citar
FERNANDES, Eduardo; WILDEMBERG, Pedro; DOS SANTOS, Jefersson.
Water Tanks and Swimming Pools Detection in Satellite Images: Exploiting Shallow and Deep-Based Strategies. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 117-122.
DOI: https://doi.org/10.5753/wvc.2020.13491.
