Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network
Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite mu...
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| Published in: | SN computer science Vol. 5; no. 8; p. 992 |
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01.12.2024
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| Abstract | Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is usedfornoisesuppressionintheremote-sensingimage.Afterthesecondstageofthe process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The proposed Long Short-Term Memory (LSTM) neural network algorithm was used to predict the flood. Implementing the proposed LSTM neural network algorithm can automatically transform large-scale data sets and implement non-linear decision-making functions through the histological characteristics of multiple layers of neurons. This work compares the accuracy of flood forecasting in the collected data set using the proposed LSTM neural network algorithm. The performance of the proposed technology is analyzed in terms of accuracy, sensitivity, and specificity. |
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| AbstractList | Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is usedfornoisesuppressionintheremote-sensingimage.Afterthesecondstageofthe process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The proposed Long Short-Term Memory (LSTM) neural network algorithm was used to predict the flood. Implementing the proposed LSTM neural network algorithm can automatically transform large-scale data sets and implement non-linear decision-making functions through the histological characteristics of multiple layers of neurons. This work compares the accuracy of flood forecasting in the collected data set using the proposed LSTM neural network algorithm. The performance of the proposed technology is analyzed in terms of accuracy, sensitivity, and specificity. |
| ArticleNumber | 992 |
| Author | Salleh, Hishamuddin Bin M. Sonavale, Amruta Chakkaravarthy, Midhun Jadhav, Jagannath Srinivasa Rao, Surampudi |
| Author_xml | – sequence: 1 givenname: Amruta surname: Sonavale fullname: Sonavale, Amruta email: amruta.ps713@gmail.com organization: Department of Computer Science and Multimedia, Lincoln University College – sequence: 2 givenname: Midhun surname: Chakkaravarthy fullname: Chakkaravarthy, Midhun organization: Faculty of Computer Science and Multimedia, Lincoln University College – sequence: 3 givenname: Surampudi surname: Srinivasa Rao fullname: Srinivasa Rao, Surampudi organization: ECE, Malla Reddy College of Engineering and Technology – sequence: 4 givenname: Hishamuddin Bin M. surname: Salleh fullname: Salleh, Hishamuddin Bin M. organization: Faculty of Computer Science and Multimedia, Lincoln University College – sequence: 5 givenname: Jagannath surname: Jadhav fullname: Jadhav, Jagannath organization: Faculty of Electronics and Communication Engineering, KLE College of Engineering and Technology |
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| Cites_doi | 10.1109/LGRS.2020.3008011 10.1109/IGARSS39084.2020.9324346 10.1109/ACCESS.2020.3027839 10.1109/IGARSS.2019.8900344 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00053 10.1109/SMARTCOMP50058.2020.00088 10.1109/IGARSS.2019.8897869 10.1109/IGARSS.2019.8899282 10.1109/IDAACS.2019.8924407 10.1109/ICAC3N53548.2021.9725595 10.1109/JSYST.2020.2984797 10.1109/ACCESS.2021.3090981 10.1109/LGRS.2018.2871849 10.1109/ICSES52305.2021.9633803 10.1109/CSICC52343.2021.9420594 |
| ContentType | Journal Article |
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| Keywords | Adaptive median filtering Flood detection Neural network algorithm LSTM Versatile Linear Edge Detection (VLED) algorithm Structural object son the RSI |
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| References_xml | – reference: Mousavi FS, Yousefi S, Abghari H and Ghasemzadeh A. Design of an IoT-based flood early detection system using machine learning. In: 26th International Computer Conference, Computer Society of Iran (CSICC), 2021;pp. 1–7. – reference: Avanzato R, Beritelli F, Cavallaro A, Cuccia M and Lombardo T. A river flood monitoring technique based on image splitting Algorithms. In: 10th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS), 2019;pp. 507–11. – reference: Ohki M and Shimada M. Flood detection in built-up area using interferometric SAR data by PALSAR-2. In: IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2019;pp. 4640–2. – reference: Lê TT, Froger J-L and Hrysiewicz A. Multiscale change analysis for SAR image time series: application to inundation detection. In: IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2019;pp. 118–21. – reference: Jo M and Osmanoglu B. Generating flood probability map based on the combined use of synthetic aperture radar and optical imagery. In: IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2020;pp. 684–7. – reference: NishanthNMujeebAModeling and detection of flooding-based denial-of-service attack in wireless Ad Hoc network using bayesian inferenceIEEE Syst J2021151172610.1109/JSYST.2020.2984797 – reference: PelichRChiniMHostacheRMatgenPLópez-MartinezCCoastline detection based on sentinel-1 time series for ship- and flood-monitoring applicationsIEEE Geosci Remote Sens Lett202118101771177510.1109/LGRS.2020.3008011 – reference: Geetha PS, Sirisha J, Swathi K and Rao PRK. GUI information system based flood detection using Gaussian distance support vector machine. In: 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021;pp. 1639–45. – reference: ZhaoMLingQLiFAn iterative feedback-based change detection algorithm for flood mapping in SAR imagesIEEE Geosci Remote Sens Lett201916223123510.1109/LGRS.2018.2871849 – reference: Samikwa E, Voigt T and Eriksson J. Flood prediction using IOT and artificial neural networks with edge computing. In: International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom), 2020;pp. 234–40. – reference: Krishna Panama VS and Rao YS. Change detection based flood mapping of 2015 flood event of Chennai city using sentinel-1 SAR images. In: IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2019;pp. 9729–32. – reference: RahnemoonfarMChowdhuryTSarkarAVarshneyDYariMMurphyRRFloodNet: a High-resolution aerial imagery dataset for post flood scene understandingIEEE Access20219896448965410.1109/ACCESS.2021.3090981 – reference: Agrawal T, Suraj and Meleet M. Classification of natural disaster using satellite and drone images with CNN using transfer learning. In: International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 2021;pp. 1–5. – reference: LeeJHongJParkGKimHSLeeSSeoTContaminated facade identification using convolutional neural network and image processingIEEE Access2020818001018002110.1109/ACCESS.2020.3027839 – reference: Basnyat B, Singh N, Roy N and Gangopadhyay A. Design and deployment of a flash flood monitoring IOT: challenges and opportunities. In: IEEE International Conference on Smart Computing (SMARTCOMP), 2020;pp. 422–7. – volume: 18 start-page: 1771 issue: 10 year: 2021 ident: 3336_CR8 publication-title: IEEE Geosci Remote Sens Lett doi: 10.1109/LGRS.2020.3008011 – ident: 3336_CR9 doi: 10.1109/IGARSS39084.2020.9324346 – volume: 8 start-page: 180010 year: 2020 ident: 3336_CR7 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3027839 – ident: 3336_CR2 doi: 10.1109/IGARSS.2019.8900344 – ident: 3336_CR11 doi: 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00053 – ident: 3336_CR12 doi: 10.1109/SMARTCOMP50058.2020.00088 – ident: 3336_CR6 doi: 10.1109/IGARSS.2019.8897869 – ident: 3336_CR1 doi: 10.1109/IGARSS.2019.8899282 – ident: 3336_CR4 doi: 10.1109/IDAACS.2019.8924407 – ident: 3336_CR13 doi: 10.1109/ICAC3N53548.2021.9725595 – volume: 15 start-page: 17 issue: 1 year: 2021 ident: 3336_CR14 publication-title: IEEE Syst J doi: 10.1109/JSYST.2020.2984797 – volume: 9 start-page: 89644 year: 2021 ident: 3336_CR15 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3090981 – volume: 16 start-page: 231 issue: 2 year: 2019 ident: 3336_CR3 publication-title: IEEE Geosci Remote Sens Lett doi: 10.1109/LGRS.2018.2871849 – ident: 3336_CR5 doi: 10.1109/ICSES52305.2021.9633803 – ident: 3336_CR10 doi: 10.1109/CSICC52343.2021.9420594 |
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| SubjectTerms | Accuracy Advances in Computational Approaches for Image Processing Algorithms Automation Back propagation Banded structure Binomial distribution Classification Cloud Applications and Network Security Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data analysis Data collection Data Structures and Information Theory Datasets Deep learning Edge detection Error analysis Error detection Floods Image enhancement Image resolution Image segmentation Impact analysis Information Systems and Communication Service Internet of Things Literature reviews Methods Neural networks Original Research Pattern Recognition and Graphics Performance evaluation Pixels Propagation Remote sensing Satellite imagery Saturation (color) Semantic segmentation Semantics Sensors Software Engineering/Programming and Operating Systems Spectrum analysis Statistical methods Technology assessment Threat evaluation Time series Vision Water Wireless Networks |
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| Title | Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network |
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