Pallavi Jain
ABSTRACT
The early and accurate detection of floods from satellite imagery can aid rescue planning and assessment of geophysical damage. Automatic identification of water from satellite images has historically relied on hand-crafted functions, but these often do not provide the accuracy and robustness needed for accurate and early flood detection. To try to overcome these limitations we investigate a tiered methodology combining water index like features with a deep convolutional neural network based solution to flood identification against the MediaEval 2019 flood dataset. Our method builds on existing deep neural network methods, and in particular the VGG16 network. Specifically, we explored different water indexing techniques and proposed a water index function with the use of Green/SWIR and Blue/NIR bands with VGG16. Our experiment shows that our approach outperformed all other water index technique when combined with VGG16 network in order to detect flood in images.
- Benjamin Bischke, Patrick Helber, Erkan Basar, Simon Brugman, Zhengyu Zhao and Konstantin Pogorelov. 2019. The Multimedia Satellite Task at MediaEval 2019: Flood Severity Estimation. In Proc. of the MediaEval 2019 Workshop. Sophia-Antipolis, France.Google Scholar
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.Google ScholarCross Ref
- Yun Du, Yihang Zhang, Feng Ling, Qunming Wang, Wenbo Li, and Xiaodong Li. 2016. Water bodies' mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing 8, 4 (2016), 354.Google ScholarCross Ref
- Arabi Mohammed El Amin, Qingjie Liu, and Yunhong Wang. 2016. Convolutional neural network features based change detection in satellite images. In First International Workshop on Pattern Recognition, Vol. 10011. International Society for Optics and Photonics, 100110W.Google Scholar
- Gudina L Feyisa, Henrik Meilby, Rasmus Fensholt, and Simon R Proud. 2014. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140 (2014), 23--35.Google ScholarCross Ref
- Gang Fu, Changjun Liu, Rong Zhou, Tao Sun, and Qijian Zhang. 2017. Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing 9, 5 (2017), 498.Google ScholarCross Ref
- Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. science 313, 5786 (2006), 504--507.Google Scholar
- Renaud Hostache, Patrick Matgen, Guy Schumann, Christian Puech, Lucien Hoffmann, and Laurent Pfister. 2009. Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods. IEEE Transactions on Geoscience and Remote Sensing 47, 2 (2009), 431--441.Google ScholarCross Ref
- Fan Hu, Gui-Song Xia, Jingwen Hu, and Liangpei Zhang. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing 7, 11 (2015), 14680--14707.Google ScholarCross Ref
- Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia. ACM, 675--678.Google ScholarDigital Library
- Sebastiaan N Jonkman and Ilan Kelman. 2005. An analysis of the causes and circumstances of flood disaster deaths. Disasters 29, 1 (2005), 75--97.Google ScholarCross Ref
- Sascha Klemenjak, Björn Waske, Silvia Valero, and Jocelyn Chanussot. 2012. Unsupervised river detection in RapidEye data. In 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 6860--6863.Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.Google Scholar
- Yann LeCun, Yoshua Bengio, et al. 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10 (1995), 1995.Google Scholar
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436.Google Scholar
- Erzhu Li, Junshi Xia, Peijun Du, Cong Lin, and Alim Samat. 2017. Integrating multilayer features of convolutional neural networks for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing 55, 10 (2017), 5653--5665.Google ScholarCross Ref
- Na Li, Arnaud Martin, and Rémi Estival. 2017. An automatic water detection approach based on Dempster-Shafer theory for multi-spectral images. In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 1--8.Google ScholarCross Ref
- Stuart K McFeeters. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing 17, 7 (1996), 1425--1432.Google ScholarCross Ref
- Renato Miceli, Igor Sotgiu, and Michele Settanni. 2008. Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of environmental psychology 28, 2 (2008), 164--173.Google ScholarCross Ref
- Kshitij Mishra and P Prasad. 2015. Automatic extraction of water bodies from Landsat imagery using perceptron model. Journal of Computational Environmental Sciences 2015 (2015).Google Scholar
- Keiller Nogueira, Waner O Miranda, and Jefersson A Dos Santos. 2015. Improving spatial feature representation from aerial scenes by using convolutional networks. In 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images. IEEE, 289--296.Google ScholarDigital Library
- Eduardo R Oliveira, Leonardo Disperati, Luca Cenci, Luísa Gomes Pereira, and Fátima L Alves. 2019. Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations. Remote Sensing 11, 11 (2019), 1305.Google ScholarCross Ref
- Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. 2014. Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1717--1724.Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Krishna Kant Singh and Akansha Singh. 2017. Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier. The Egyptian Journal of Remote Sensing and Space Science 20, 1 (2017), 147--155.Google ScholarCross Ref
- Laurence C Smith. 1997. Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological processes 11, 10 (1997), 1427--1439.Google ScholarCross Ref
- Guerschman JP Ticehurst C and Chen Y. 2014. The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events. Remote Sensing 6, 12 (2014), 11791--11809.Google ScholarCross Ref
- Yunchao Wei, Wei Xia, Min Lin, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, and Shuicheng Yan. 2015. HCP: A flexible CNN framework for multi-label image classification. IEEE transactions on pattern analysis and machine intelligence 38, 9 (2015), 1901--1907.Google Scholar
- Hanqiu Xu. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing 27, 14 (2006), 3025--3033.Google Scholar
Index Terms
- Automatic flood detection in SentineI-2 images using deep convolutional neural networks
Index terms have been assigned to the content through auto-classification.
Recommendations
Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks
ACPR '13: Proceedings of the 2013 2nd IAPR Asian Conference on Pattern RecognitionDeep convolutional Neural Networks (DNN) is the state-of-the-art machine learning method. It has been used in many recognition tasks including handwritten digits, Chinese words and traffic signs, etc. However, training and test DNN are time-consuming ...
Deep Convolutional Neural Networks for Large-scale Speech Tasks
Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize ...
Convolutional Neural Networks based classifications of soil images
AbstractThe utilization of Artificial Intelligence (AI) and Machine Learning(ML) in the image processing domain is useful to detect and recognize the types of soil. The main aim of the present work is to process the soil images and classify them ...
Comments