Browsing by Author "Hegde, A.V."

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A comparative study on extraction of buildings from Quickbird-2 satellite imagery with & without fusion
(Cogent OA info@CogentOA.com, 2017) Pushparaj, J.; Hegde, A.V.
Extraction of building from very high resolution satellite imagery is a challenging task. Many automatic algorithms are proposed to extract buildings from remote sensing imageries, but most of the algorithms detect only rectangular buildings very effectively (i.e. buildings with the same size and shape). In this paper, an attempt is made to extract buildings with different shape, size, color and pattern from Quickbird-2 imagery. In the automatic method, firstly the adaptive k means clustering algorithm is performed to classify the pixels into a number of classes which then is followed by morphological operators to extract the buildings. The manual method is also implemented to extract building feature. Consequently, both, the automatic and manual methods are adopted on the original Multispectral (MS) image and on the fused image obtained by fusing Quickbird-2 Panchromatic (Pan) image with MS image using the Fuze Go method. The performance of both the methods for the extraction of buildings is evaluated using qualitative and metric analysis. The experimental results show that both the methods are performed reasonably well. However, improving the spatial resolution of the original MS image by fusion helps to determine the buildings information more precisely in terms of spatially as well as spectrally. © 2017 The Author(s).
Application of Neural Networks in coastal engineering - An overview
(2008) Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.
Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output system. In addition, ANNs have a very high degree of freedom and are very simple to train the system for any number of input values, which makes the network attractive and reliable. ANNs are ideally suited to find many solutions like pattern reorganization, data classification, forecasting future events and time series analysis. This paper gives an overview of application of ANN in the field of coastal engineering.
Application of Neural Networks in coastal engineering - An overview
(2008) Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.
Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output system. In addition, ANNs have a very high degree of freedom and are very simple to train the system for any number of input values, which makes the network attractive and reliable. ANNs are ideally suited to find many solutions like pattern reorganization, data classification, forecasting future events and time series analysis. This paper gives an overview of application of ANN in the field of coastal engineering.
Bathymetry mapping using landsat 8 satellite imagery
(2015) Jagalingam, P.; Akshaya, B.J.; Hegde, A.V.
Bathymetry is the science of determining the topography of the seafloor. Bathymetry data is used to generate navigational charts, seafloor profile, biological oceanography, beach erosion, sea level rise, etc. A number of methods are available for determining ocean bathymetry, using either active sensor such as sonar, lidar or passive multispectral imagery such as Ikonos, WorldView and Landsat. Determining the bathymetry using sonar and LiDAR is very expensive, while Ikonos and Worldview are commercially available multispectral satellite platforms whereas Landsat satellite imagery provides a free and publicly available data. Therefore, the present study makes an attempt to determine the bathymetry mapping of the southwest coast of India (13� 0' 0" N and 74�50' 0" E) by applying the ratio transform algorithm on the blue and green bands of Landsat 8 satellite imagery. The statistical indices such as R2, RMSE and MAE are computed between the algorithm derived value and the hydrographic chart sounding value. The result shows a good correlation between the algorithm derived value and hydrographic chart sounding value. � 2015 The Authors. Published by Elsevier Ltd.
Bathymetry mapping using landsat 8 satellite imagery
(Elsevier Ltd, 2015) Pushparaj, P.; Akshaya, B.J.; Hegde, A.V.
Bathymetry is the science of determining the topography of the seafloor. Bathymetry data is used to generate navigational charts, seafloor profile, biological oceanography, beach erosion, sea level rise, etc. A number of methods are available for determining ocean bathymetry, using either active sensor such as sonar, lidar or passive multispectral imagery such as Ikonos, WorldView and Landsat. Determining the bathymetry using sonar and LiDAR is very expensive, while Ikonos and Worldview are commercially available multispectral satellite platforms whereas Landsat satellite imagery provides a free and publicly available data. Therefore, the present study makes an attempt to determine the bathymetry mapping of the southwest coast of India (13Â° 0' 0" N and 74Â°50' 0" E) by applying the ratio transform algorithm on the blue and green bands of Landsat 8 satellite imagery. The statistical indices such as R2, RMSE and MAE are computed between the algorithm derived value and the hydrographic chart sounding value. The result shows a good correlation between the algorithm derived value and hydrographic chart sounding value. Â© 2015 The Authors. Published by Elsevier Ltd.
Beyond the data range approach to soft compute the reflection coefficient for emerged perforated semicircular breakwater
(Springer, 2019) Kundapura, S.; Hegde, A.V.; Wazerkar, A.V.
Prediction of reflection coefficient (Kr) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (Kr) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (Hi, T, S, D, R, d, hs) that influence the reflection coefficient (Kr) are considered for training as well as testing, where Hi is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and hs is the structure height. The accuracy of predictions of reflection coefficient (Kr) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient Kr of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of Kr in the case of beyond the data range segregation method. © Springer Nature Singapore Pte Ltd. 2019.
Coastal erosion and mitigation methods - global state of art
(2010) Hegde, A.V.
Coastal erosion is assuming large proportions these days. Global climate change coupled with local attributes is eroding the coasts of the world in alarming proportions. Most of the conventional protection methods are hard, short lived, expensive and not eco-friendly. Trend in coastal erosion mitigation and protection has been shifting these days towards soft but novel, eco-friendly methods. Pro-active methods are being developed and used which are eco-friendly, construction-friendly, cheaper and which also reasonably address the root cause of the problem without much 'side effects'. Many non-traditional ways to armor, stabilize or restore beaches, including the use of patented precast concrete units, geotextile sand-filled bags, green belts, bio-engineering, sand fencing, beach-face dewatering systems, integrated costal protection methods are being used. Retreat from the coast is also thought about, in many circles. Present study consists the global coastal erosion scenario and also some of the state of the art soft and pro-active erosion mitigation methods.
Comparative study of ocean wave spectrum using ENVISAT SAR data and wave rider buoy data
(2006) Pai, B, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.
A comparative study of ENVISAT ASAR data and corresponding wave rider buoy data has been attempted. An algorithm has been developed to retrieve Ocean Wave Spectrum from SAR data. The resulting spectrum is compared with the wave rider buoy measured wave spectrum. To compute the 2-D image spectrum from multi-look SAR data, various corrections to the original SAR data has been applied. Thereafter, Modulation Transfer Function has been computed and utilized to convert image spectrum to the Ocean Wave Spectrum. This final ocean wave height spectrum is used to estimate the ocean wave spectral parameters and has been compared with the in-situ measurements and model derived wave spectrum. An attempt has also been made to process the Single Look Complex (SLC) data to reduce the speckle noise in the SAR data using Fast Fourier Transform (FFT).
Comparative study of ocean wave spectrum using ENVISAT SAR data and wave rider buoy data
(2006) Pai, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.
A comparative study of ENVISAT ASAR data and corresponding wave rider buoy data has been attempted. An algorithm has been developed to retrieve Ocean Wave Spectrum from SAR data. The resulting spectrum is compared with the wave rider buoy measured wave spectrum. To compute the 2-D image spectrum from multi-look SAR data, various corrections to the original SAR data has been applied. Thereafter, Modulation Transfer Function has been computed and utilized to convert image spectrum to the Ocean Wave Spectrum. This final ocean wave height spectrum is used to estimate the ocean wave spectral parameters and has been compared with the in-situ measurements and model derived wave spectrum. An attempt has also been made to process the Single Look Complex (SLC) data to reduce the speckle noise in the SAR data using Fast Fourier Transform (FFT).
Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery
(Springer Verlag service@springer.de, 2017) Pushparaj, J.; Hegde, A.V.
Pan-sharpening is the process of transferring the spatial resolution of panchromatic (PAN) image to a multispectral (MS) image for producing a single image with high spatial detail and rich spectral information. In this study, PAN and MS imagery of Quickbird-2 and Landsat-8 are fused separately, using ten different pan-sharpening methods such as principal component analysis (PCA), modified-intensity hue saturation (M-IHS), multiplicative, brovey transform (BT), wavelet-principal component analysis (W-PCA), hyperspectral color space (HCS), high-pass filter (HPF), Gram-Schmidt (GS), Fuze Go, and non-subsampled contourlet transform (NSCT). The effectiveness of these techniques is assessed and compared by qualitative analysis and 14 quantitative analysis methods including bias, correlation coefficient (CC), difference in variance (DIV), relative dimensionless global error in synthesis (ERGAS), universal image quality index (Q), relative average spectral error (RASE), root mean square error (RMSE), structural similarity index method (SSIM), signal-to-noise ratio (SNR), peak SNR (PSNR), spatial correlation coefficient (SCC), image entropy (E), and gradient and quality with no reference image (QNR). The results of both analysis types show that the Fuze Go and NSCT produced the best fused image with high spatial detail and rich spectral information followed by the HPF and GS. © 2017, Saudi Society for Geosciences.
Computational intelligence on hydrodynamic performance characteristics of Emerged Perforated Quarter Circle Breakwater
(2015) Raju, B.; Hegde, A.V.; Chandrashekar, O.
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwater (EPQCB) is an artificial concrete breakwater consisting of a curved perforated face fronting the waves, a vertical wall on back and a base slab resting on a low rubble mound base. The perforated curved front face is having advantages like energy dissipation and good stability with less material as it is hollow inside. Computational Intelligence (CI) can be adopted for the evaluation of performance characteristics like reflection, dissipation, run-up and rundown which are complex, time consuming and expensive to perform in laboratory. The paper presents the work carried out to predict the reflection coefficient (Kr) for input parameters, wave period (T) beyond the data range used for training and of wave height (H) along with the data on input parameters of water depth (d), spacing-perforation ratio (S/D) and radius (R) of the EPQCB. The data on various parameters are taken in two categories for training and testing of ANN as mentioned below in order to understand the effect of using non-dimensional data in place of parametric values: 1) Input in the form of parametric data (H, T, d, R, S, D), and 2) Input in the form of non-dimensional values (H/gT2, d/gT2, S/D, R/H). Better correlation was found when individual dimensional parametric data was used instead of non-dimensional group values in both the methods of prediction. Similarly, the correlation between the beyond the data range prediction and actual values was found to be good in both methods of prediction. � 2015 The Authors. Published by Elsevier Ltd.
Computational intelligence on hydrodynamic performance characteristics of Emerged Perforated Quarter Circle Breakwater
(Elsevier Ltd, 2015) Raju, B.; Hegde, A.V.; Sekhar, O.
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwater (EPQCB) is an artificial concrete breakwater consisting of a curved perforated face fronting the waves, a vertical wall on back and a base slab resting on a low rubble mound base. The perforated curved front face is having advantages like energy dissipation and good stability with less material as it is hollow inside. Computational Intelligence (CI) can be adopted for the evaluation of performance characteristics like reflection, dissipation, run-up and rundown which are complex, time consuming and expensive to perform in laboratory. The paper presents the work carried out to predict the reflection coefficient (Kr) for input parameters, wave period (T) beyond the data range used for training and of wave height (H) along with the data on input parameters of water depth (d), spacing-perforation ratio (S/D) and radius (R) of the EPQCB. The data on various parameters are taken in two categories for training and testing of ANN as mentioned below in order to understand the effect of using non-dimensional data in place of parametric values: 1) Input in the form of parametric data (H, T, d, R, S, D), and 2) Input in the form of non-dimensional values (H/gT2, d/gT2, S/D, R/H). Better correlation was found when individual dimensional parametric data was used instead of non-dimensional group values in both the methods of prediction. Similarly, the correlation between the beyond the data range prediction and actual values was found to be good in both methods of prediction. Â© 2015 The Authors. Published by Elsevier Ltd.
Conventional prediction vs beyond data range prediction of loss coefficient for quarter circle breakwater using ANFIS
(2015) Hegde, A.V.; Raju, B.
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and also to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwaters (EPQCB) are artificial concrete breakwaters consisting of a curved perforated face fronting the waves with a vertical wall on rear side and a base slab resting on a low rubble mound base. The perforated curved front face has advantages like energy dissipation and good stability with less material as it is hollow inside. The estimation of hydrodynamic performance characteristics of EPQCB by physical model studies is complex, expensive and time consuming. Hence, computational intelligence (CI) methods are adopted for the evaluation of the performance characteristics like reflection, dissipation, transmission, runup, rundown etc. A number of CI methods like Artificial Neural Network (ANN), Fuzzy logic, and hybrids such as ANFIS, ANN-PCO (particle swarm optimization), ANN-ACO etc., are available and are being used. The paper presents the work carried out to predict the dependent output variable of loss coefficient (Kl) beyond the range of values of one of the input variables i.e., wave period (T) adopted in present work, using the input data on variables of wave height (H), wave period (T), structure height (hs), water depth (d), radius of the breakwater (R), spacing of perforations (S) and diameter of perforations (D) using ANFIS. For this purpose, both the conventional method of data segregation and also a new method called �beyond data range� method are used for both training the ANFIS models and also to predict the dependent variable. Further, the input data was fed to the models in both dimensional and nondimensional form in order to understand the effect of using non-dimensional data in place of dimensional parametric data. The performance of ANFIS models for all the four cases mentioned above was studied and it was found that prediction using conventional method with non-dimensional parameters performed better than other three methods. ANFIS models can be used to predict the performance characteristic Kl of EPQCB beyond the input data range of wave period T. � Springer International Publishing Switzerland 2015.
Conventional prediction vs beyond data range prediction of loss coefficient for quarter circle breakwater using ANFIS
(Springer Verlag service@springer.de, 2015) Hegde, A.V.; Raju, B.
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and also to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwaters (EPQCB) are artificial concrete breakwaters consisting of a curved perforated face fronting the waves with a vertical wall on rear side and a base slab resting on a low rubble mound base. The perforated curved front face has advantages like energy dissipation and good stability with less material as it is hollow inside. The estimation of hydrodynamic performance characteristics of EPQCB by physical model studies is complex, expensive and time consuming. Hence, computational intelligence (CI) methods are adopted for the evaluation of the performance characteristics like reflection, dissipation, transmission, runup, rundown etc. A number of CI methods like Artificial Neural Network (ANN), Fuzzy logic, and hybrids such as ANFIS, ANN-PCO (particle swarm optimization), ANN-ACO etc., are available and are being used. The paper presents the work carried out to predict the dependent output variable of loss coefficient (Kl) beyond the range of values of one of the input variables i.e., wave period (T) adopted in present work, using the input data on variables of wave height (H), wave period (T), structure height (hs), water depth (d), radius of the breakwater (R), spacing of perforations (S) and diameter of perforations (D) using ANFIS. For this purpose, both the conventional method of data segregation and also a new method called â€˜beyond data rangeâ€™ method are used for both training the ANFIS models and also to predict the dependent variable. Further, the input data was fed to the models in both dimensional and nondimensional form in order to understand the effect of using non-dimensional data in place of dimensional parametric data. The performance of ANFIS models for all the four cases mentioned above was studied and it was found that prediction using conventional method with non-dimensional parameters performed better than other three methods. ANFIS models can be used to predict the performance characteristic Kl of EPQCB beyond the input data range of wave period T. Â© Springer International Publishing Switzerland 2015.
Current approaches of artificial intelligence in breakwaters - A review
(Techno Press technop2@chollian.net, 2017) Kundapura, S.; Hegde, A.V.
A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables. © 2017 Techno-Press, Ltd.
Determination of mixed layer depth from C-Band Synthetic Aperture Radar (SAR)
(2010) Pai, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.
Oceanic internal waves are frequently observed on the continental shelf during the summer season, when the ocean is stratified. The appearance of internal wave phenomena in remote sensing images has been increasing the curiosity to observe internal wave at specific area in the world. Studies reveal that Synthetic Aperture Radar has a capability to detect internal waves. In the present study, ENVISAT Advanced Synthetic Aperture Radar (ASAR) image acquired on October 4, 2003, was used to determine Mixed Layer Depth (MLD) off Bay of Bengal of Indian Ocean region. The image showed several prominent trains of internal waves, with several wave packets in each train. The ocean was assumed to be a two layer system, and that the local semidiurnal tide is the generating force for the internal waves. By assuming that the local semidiurnal tide period is the generating source for these waves, and by measuring the distance between the wave packets, it is possible to derive the group velocity of the internal waves from Synthetic Aperture Radar (SAR) images directly. The mixed -layer depth is then derived by assuming the ocean as a two-layer finite depth model. The group velocity measured from the SAR image and the simulated group velocity by the two layer finite depth model was matched to get the mixed layer depth. The estimated mixed layer depth was 21m. This value show reasonably good agreement with the actual depth of 19.5m of in-situ ARGO buoy. © 2010 by IJI (CESER Publications).
Development of coastal vulnerability index for Mangalore coast, India
(2007) Hegde, A.V.; Reju, V.R.
The paper presents the coastal vulnerability index (CVI) for the estimation of vulnerability of the coastal region of Mangalore coast, India, from Talapady to Surathkal. The CVI is an indication of the relative vulnerability of the various segments of the Mangalore coast to coastal erosion hazards. The following variables are used in the estimation of CVI, which is used to rank the vulnerability of the coastal regions: geomorphology, regional coastal slope, shoreline change rates, and population. The rankings for each variable were combined and an index value calculated for 1? X 1? grid cells covering the study area.
Development of prediction models for hydrodynamic performance of semicircular breakwater
(2012) Aggarwal, A.; Gope, V.K.; Managiri, S.S.; Hegde, A.V.
Breakwaters are structures built to protect harbors, shore areas, basins, and other areas from the fury of sea waves. They create calm waters and provide for the safe mooring and handling of ships, as well as protection to harbor facilities. The main function of a breakwater is the formation of an artificial harbor. Of late, certain new types of breakwaters have been constructed to cater to the tranquility requirements of managing marine traffic in ports. The semicircular breakwater (SBW) is one such new type of breakwater. The semicircular breakwater possesses a round top and, thus, offers more stability against the action of waves. It is expected that the SBW will be well suited as an offshore breakwater designed to protect beaches from coastal erosion. A number of experiments were conducted on scaled-down physical models of SBW for different values of parameters such as wave height H, wave period T, spacing of perforations on the seaside, etc. (radius of breakwater and diameter of perforations were kept constant), and data were collected. The paper presents the prediction models/equations for hydrodynamic performance characteristics such as reflection coefficient and relative wave runup, using the data obtained by a regression approach in MATLAB.
Effect of water depth on wave reflection and loss characteristics of an emerged perforated quarter circle breakwater
(CESER Publications Post Box No. 113 Roorkee 247667, 2016) Shahulhameed, S.; Hegde, A.V.; Rao, S.
Quarter circle breakwater is a new-type breakwater first proposed by Xie et al. (2006) on the basis of semicircular breakwater. Quarter circle breakwater is usually placed on rubble mound foundation and its superstructure consists of a precast reinforced concrete quarter circular surface facing incident waves, a horizontal bottom slab and a rear vertical wall. A series of experiments were conducted in a two dimensional monochromatic wave flume on a seaside perforated quarter circle breakwater model. The present study investigates the wave reflection and loss characteristics on an emerged seaside perforated quarter circle breakwater of three different radii and with ratio of spacing to diameter of perforations equal to 5, for different water depths and wave conditions. The results were plotted as non-dimensional graphs and it was observed that the reflection coefficient increases with increase in wave steepness and increase in ratio of height of breakwater structure to water depth. It was also found that the loss coefficient decreases with increase in wave steepness and increase in ratio of height of breakwater structure to water depth. © 2016 IJED.
Evaluation of hydrodynamic performance of quarter circular breakwater using soft computing techniques
(Springer, 2019) Ramesh, N.; Hegde, A.V.; Rao, S.
Breakwaters are massive structures constructed to provide the required tranquility within the ports. They are also used for safeguarding the beaches from eroding due to the severe action of waves, especially during inclement weather. In recent years, innovative structures such as Semi-circular and Quarter-circular Breakwaters (QBW) are being evolved to fulfill the ever-increasing demand from the coastal sector. QBW is a caisson with quarter circular surface towards incident waves, with horizontal bottom and a vertical wall on its rear side placed on a rubble mound foundation. In this paper, the experimental data collected at National Institute of Technology, Surathkal is used. The data collected is analysed by plotting the non-dimensional graphs of reflection coefficient, reflected wave height and incident wave height for various values of wave steepness. The values are used for prediction of QBW adopting Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Goodness-of-Fit (GoF) test using Kolmogorov–Smirnov (KS) test statistic is applied for checking the adequacy of MLP and RBF networks to the experimental data. The performance of these networks is evaluated by using Model Performance Indicators (MPIs), viz. correlation coefficient, mean absolute error and model efficiency. The GoF test results and values of MPIs indicated the MLP is better suited amongst two networks adopted for evaluation of hydrodynamic performance of QBW. © Springer Nature Singapore Pte Ltd. 2019.