Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images
Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images
In remote sensing applications, segmentation of input satellite images according to semantic information and estimating the semantic category of each pixel from a given set of tags are of great importance for the automatic tracking task. It is important in situations such as building detection from high resolution satellite images, city planning, environmental preparation, disaster management. Buildings in metropolitan areas are crowded and messy, so high-resolution images from satellites need to be automated to detect buildings. Segmentation of remote sensing images with deep learning technology has been a widely considered area of research. The Fully Convolutional Network (FCN) model, a popular segmentation model, is used for building detection based on pixel-level satellite images. In the U-Net model developed for biomedical image segmentation and modified in our study, its performances during training, accuracy and testing were compared by using customized loss functions such as Dice Coefficient and Jaccard Index measurements. Dice Coefficient loss score was obtained 84% and Jaccard Index lost score was obtained 70%. In addition, the Dice Coefficient loss score increased from 84% to 87% by using the Batch Normalization (BN) method instead of the Dropout method in the model.
___
- [1] Q, Han, Q. Yin, X. Zheng, Z. Chen, “Remote sensing image building
detection method based on Mask R-CNN.” Complex & Intelligent
Systems, 8(3), 1847-1855, 2022.
- [2] M. Ataş, “Fıstık sınıflandırma sistemi için Siirt fıstığı imgelerinden
gürbüz özniteliklerin çıkarılması.” Dicle Üniversitesi Mühendislik
Fakültesi Mühendislik Dergisi 7(1):93-102, 2016.
- [3] E. Acar, “Detection of unregistered electric distribution transformers in
agricultural fields with the aid of Sentinel-1 SAR images by machine
learning approaches.” Computers and Electronics in Agriculture, 175,
105559, 2020.
- [4] A. D. Yetis, M. I. Yesilnacar, M. Atas, “A machine learning approach
to dental fluorosis classification.” Arabian Journal of
Geosciences, 14(2):1-12, 2021.
- [5] M. Atas, Y. Dogan, İ. Atas, “Chess playing robotic arm.” In 2014 22nd
Signal Processing and Communications Applications Conference
(SIU) (pp. 1171-1174). IEEE, 2014.
- [6] C. Özdemı̇ r, M. Ataş, A. B. Özer, “Classification of Turkish spam emails
with artificial immune system.” 21st Signal Processing and
Communications Applications Conference (SIU). IEEE, 2013.
- [7] S. Ji, S. Wei, M. Lu, “Fully convolutional networks for multisource
building extraction from an open aerial and satellite imagery
dataset.” IEEE Transactions on Geoscience and Remote Sensing, 57(1),
574-586, 2018.
- [8] Ç. Kaymak, A. Uçar, “Semantic Image Segmentation for Autonomous
Driving Using Fully Convolutional Networks.” International Artificial
Intelligence and Data Processing Symposium (IDAP), 2019.
DOI: 10.1109/IDAP.2019.8875923.
- [9] A. Valizadeh, M. Shariatee, “The Progress of Medical Image Semantic
Segmentation Methods for Application in COVID-19 Detection.”
Comput Intell Neurosci. 2021, DOI: 10.1155/2021/7265644.
- [10] A. Mousavian, J. Kosecka, “Semantic Image Based Geolocation Given
a Map.” DOI: 10.48550/arXiv.1609.00278.
- [11] T. Anand, S. Sinha, M. Mandal, V. Chamola, F. R. Yu, “AgriSegNet:
Deep aerial semantic segmentation framework for IoT-assisted
precision agriculture.” IEEE Sensors Journal, 21(16), 17581-17590,
2021.
- [12] W. Wu et al., “Building extraction from high resolution remote sensing
imagery based on spatial-spectral method.”, Geomat Inf Sci Wuhan
Univ 7:800–805, 2012.
- [13] X. Huang et al., “Classification of high spatial resolution remotely
sensed imagery based upon fusion of muitiscale features and SVM.”, J
Remote Sens 11:48–54, 2007.
- [14] F. Xin, C. Shanxiong, “High-resolution remote sensing image building
extraction in dense urban areas.” Bull Surv Mapp, 2019.
- [15] H. Acar, M. S. Özerdem, E. Acar, “Soil moisture inversion via
semiempirical and machine learning methods with full-polarization
Radarsat-2 and polarimetric target decomposition data: A comparative
study.” IEEE Access, 8, 197896-197907, 2020.
- [16] W. Xu-dong, G. Jian-ming, J. Bai-jun et al., “Mixed-pixel classification
of remote sensing images of cellular automata.”, J Surv Mapp
37(1):42–48, 2008.
- [17] G. Wu, X. Shao, Z. Guo, Q. Chen, W. Yuan, X. Shi, et al. “Automatic
building segmentation of aerial imagery using multi-constraint fully
convolutional networks”, Remote Sensing, 10, p. 407, 2018.
- [18] J. Yuan, “Learning building extraction in aerial scenes with
convolutional networks.”, IEEE Transactions on Pattern Analysis
Machine Intelligence, 40, pp. 2793-2798, 2017.
- [19] Q. Chen, L. Wang, Y. Wu, G. Wu, Z. Guo, S. L. Waslander, “Aerial
imagery for roof segmentation: A large-scale dataset towards automatic
mapping of buildings.”, ISPRS Journal of Photogrammetry and Remote
Sensing, 147, pp. 42-55, 2018.
- [20] http://study.rsgis.whu.edu.cn/pages/download/
- [21] O. Ronneberger, P. Fischer, T. Brox, “U-net: Convolutional networks for biomedical image segmentation.” International conference on medical image computing and computer-assisted intervention, (pp. 234–241). Springer, 2015.
- [22] J. Long, E. Shelhamer, T. Darrell, “Fully Convolutional Networks for Semantic Segmentation”, University of Berkeley, Proceedings of the IEEE, 2015.
- [23] N. Ketkar, “Stochastic gradient descent.”, In Deep learning with Python (pp. 113-132). Apress, Berkeley, CA, 2017.
- [24] A. Radford, L. Metz, S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks.”, arXiv preprint arXiv:1511.06434, 2015.
- [25] F. Chollet, "Keras: Deep learning library for theano and tensorflow", 2015, [online] Available: https://github.com/fchollet/keras.
- [26] Google Colab [Online] Access Link:
https://colab.research.google.com/, on 21 November 2022.
- [27] G. Chhor, B. A. Cristian, B-L. Ianis, "Satellite image segmentation for building detection using U-Net." Web: http://cs229. stanford. edu/proj2017/final-reports/5243715.pdf, 2017.
- [28] İ. Ataş, “Human gender prediction based on deep transfer learning from panoramic dental radiograph images.” Traitement du Signal, 39(5), 1585-1595, 2022. DOI:10.18280/ts.390515
- [29] A. H. Murphy, "The Finley Affair: A Signal Event in the History of Forecast Verification." Weather and Forecasting. 11 (1): 3, 1996.
- [30] Jaccard, Paul, "The Distribution of the Flora in the Alpine Zone.1". New Phytologist. 11 (2): 37–50, 1912. DOI:10.1111/j.1469-8137.1912.tb05611.x.
- [31] T. Sørensen, "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons." Kongelige Danske Videnskabernes Selskab. 5 (4): 1–34, 1948.
- [32] L. R. Dice, "Measures of the Amount of Ecologic Association Between Species." Ecology. 26 (3): 297–302, 1945.DOI:10.2307/1932409.
- [33] F. Milletari, N. Navab, S. A. Ahmadi, “V-Net: Fully convolutional neural networks for volumetric medical image segmentation.” In Proceedings of the 14th 3D Vision, Stanford, CA, USA, 25–28, pp. 565–571, 2016.
- [34] J. Zhang, et al. "Segmenting purple rapeseed leaves in the field from UAV RGB imagery using deep learning as an auxiliary means for nitrogen stress detection." Remote Sensing 12.9, 1403, 2020.
- [35] J. Ma, et al., “Building Extraction of Aerial Images by a Global and Multi-Scale Encoder Decoder Network.” Remote Sens., 12, 2350, 2020.
- [36] J. Lin, W. Jing, H. Song, G. Chen, “ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images.” IEEE Access, 7, 54285–54294, 2019.
- [37] V. Iglovikov, A. Shvets, “Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation.” arXiv preprint arXiv:1801.05746, 2018.
- [38] G. Chhor, C. B. Aramburu, I. Bougdal-Lambert, Satellite image segmentation for building detection using U-Net. Web: http://cs229. stanford. edu/proj2017/final-reports/5243715, 2017.
- [39] D. Patil, K. Patil, R. Nale, S. Chaudhari, "Semantic Segmentation of Satellite Images using Modified U-Net," IEEE 10. Regional Symposium (TENSYMP), s.1-6, 2022.