Detecting floods using an object based change detection approach

Bukenya, Faiza; Yuhaniz, Siti Sophiayati; Hashim, Siti Zaiton Mohd; Kalema, A. K.

doi:10.1109/iccce.2012.6271149

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…In the literature, the authors have mainly focussed on change detection occurring due to disasters, depending only on sensors [32] and used the suitable image processing techniques [22] such as image algebra (band differencing and band rationing) [30], post-classification comparison and object-based change detection method [11]. In recent years researchers are focusing on machine learning techniques for accurate automatic detection of the disaster region compared to the normal methodologies adapted previously [1,10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The average testing time for Feed-Forward Neural Network is 0.68 seconds and for Cascade-forward back propagation Neural Network is 0.58 seconds as presented in Figure 10. The images used in this study are microwave images obtained by Sentinel-1 instrument in Interferometric Wide (IW) swath mode [11] (refer Figures 5 (a), (b), (c), (d) & (e)). The machine learning algorithms are implemented in this study of a flood that occurred in Uttar Pradesh, India in August 2017.…”

Section: Testing Phasementioning

confidence: 99%

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Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

Nallapareddy

Balakrishnan²

2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Testing Phasementioning

confidence: 99%

Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

Nallapareddy

Balakrishnan²

2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Additionally, they can be affected by noise, in particular, when handling the rich information content of high resolution data [19]. For the object-based methods [20][21][22][23][24], first a segmentation procedure is applied to extract the features of ground objects. Next, using an object-based comparison process the change map is obtained.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

Gargees

Scott

2021

Remote Sensing

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In the era of big data, where massive amounts of remotely sensed imagery can be obtained from various satellites accompanied by the rapid change in the surface of the Earth, new techniques for large-scale change detection are necessary to facilitate timely and effective human understanding of natural and human-made phenomena. In this research, we propose a chip-based change detection method that is enabled by using deep neural networks to extract visual features. These features are transformed into deep orthogonal visual features that are then clustered based on land cover characteristics. The resulting chip cluster memberships allow arbitrary level-of-detail change analysis that can also support irregular geospatial extent based agglomerations. The proposed methods naturally support cross-resolution temporal scenes without requiring normalization of the pixel resolution across scenes and without requiring pixel-level coregistration processes. This is achieved with configurable spatial locality comparisons between years, where the aperture of a unit of measure can be a single chip, a small neighborhood of chips, or a large irregular geospatial region. The performance of our proposed method has been validated using various quantitative and statistical metrics in addition to presenting the visual geo-maps and the percentage of the change. The results show that our proposed method efficiently detected the change from a large scale area.

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“…According to the availability of the training samples, CD methods can be broadly grouped into two categories: 1) supervised [6]- [10] and 2) unsupervised [11]- [14]. Supervised CD methods exploit the learning capacity of classifiers to extract change information, and are generally robust to different atmospheric conditions and acquisition dates.…”

mentioning

confidence: 99%

Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral–Spatial Features

Tan

Xiao

Plaza

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Change detection (CD) is an active research topic in remote sensing applications including urban studies, disaster assessment, and deforestation monitoring. In this paper, we propose an automatic method for CD in high-resolution remote sensing images that uses a novel strategy for the selection of training samples and an ensemble of multiple classifiers. As for the selection of training samples, our proposed method uses two groups of thresholds instead of just one threshold to enhance the quality of the selected training samples by allowing for their selection in an intelligent manner. In order to achieve high CD accuracy, spatial information such as texture and morphological profiles are utilized in conjunction with spectral information. Our multiple classifier system (MCS) exploits the extreme learning machine (ELM), multinomial logistic regression (MLR), and K-nearest neighbor (KNN) classifiers. To validate our newly proposed approach, we conduct experiments using multispectral images collected by ZY-3. The proposed method provides state-of-the-art CD accuracies as compared with other approaches widely used in the literature for CD purposes.

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Detecting floods using an object based change detection approach

Cited by 10 publications

References 16 publications

Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral–Spatial Features

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