Faculty Publications
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Item Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction(Indian Academy of Sciences, 2013) Senthilnath, J.; Handiru, H.V.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping. © Indian Academy of Sciences.Item Guided SAR image despeckling with probabilistic non local weights(Elsevier Ltd, 2017) Gokul, J.; Nair, M.S.; Rajan, J.SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method. © 2017 Elsevier LtdItem Adaptive non-local level-set model for despeckling and deblurring of synthetic aperture radar imagery(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Padikkal, P.; Banothu, B.In this article, we modify Mumford–Shah level-set model to handle speckles and blur in synthetic aperture radar (SAR) imagery. The proposed model is formulated using a non-local regularization framework. Hence, the model duly cares about local gradient oscillations (corresponding to the fine details/textures) during the evolution process. It is assumed that the speckle intensity is gamma distributed, while designing a maximum a posteriori estimator of the functional. The parameters of the gamma distribution (i.e. scale and shape) are estimated using a maximum likelihood estimator. The regularization parameter of the model is evaluated adaptively using these (estimated) parameters at each iteration. The split-Bregman iterative scheme is employed to improve the convergence rate of the model. The proposed and the state-of-the-art despeckling models are experimentally verified and compared using a large number of speckled and blurred SAR images. Statistical quantifiers are used to numerically evaluate the performance of various models under consideration. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.Item Hierarchical clustering approaches for flood assessment using multi-sensor satellite images(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Sundaram, S.; Kulkarni, S.; Benediktsson, J.A.In this paper, hierarchical clustering methods are used on synthetic aperture radar (SAR) (during the flood) and LISS-III (before the flood) data to analyse damage caused by floods. The flooded and non-flooded regions are extracted from the SAR image while different land cover regions are extracted from the LISS-III image. Initially, the Bayesian information criterion (BIC) is implemented to obtain the constraints for the number of clusters. The optimal cluster centres are then computed using hierarchical clustering approach (i.e. cluster splitting and merging techniques). The cluster splitting techniques such as Iterative Self-Organising Data Technique (ISODATA), Mean Shift Clustering (MSC), Niche Genetic Algorithm (NGA) and Niche Particle Swarm Optimisation (NPSO) were applied on SAR and LISS-III data. The cluster centres obtained from these algorithms are used to group similar data points by using merging method into their respective classes. Further, the results obtained for each method are overlaid to analyse the individual land cover region that is affected by floods. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.Item Modeling of surface soil moisture using C-band SAR data over bare fields in the tropical semi-arid region of India(Springer Science and Business Media Deutschland GmbH, 2021) Gururaj, P.; Umesh, P.; Shetty, A.Spatial variability of surface soil moisture is a prime factor in modeling many environmental and meteorological processes. This study aims to model surface soil moisture in bare fields using Sentinel-1A SAR data at a regional scale. The site/plot selected for the study falls in the tropical semi-arid region of Malavalli, Karnataka, India. The study site is divided into 43 grids to collect soil moisture samples from bare field plots synchronized with Sentinel-1A pass. Sentinel-1A, dual-polarized (VV and VH) data with 5.405-GHz frequency and central incidence angle of 33° are used. Six SAR imageries were procured from ESA, out of which five were used to model field soil moisture and one for validation. Processing of the SAR imageries is carried out using SNAP 7.0 software’s standard tools, and the backscattered energy of each sample grid is extracted using R software. The relation between SAR backscatter energy with soil parameters like moisture, dielectric constant, and roughness was used to model soil moisture. Results revealed that Sentinel-1A has a high potential to record the soil moisture spatial variation at the plot scale. Volumetric soil moisture and backscattered energy showed a positive correlation with R2 of 0.59 and 0.51 for VV and VH polarization. Dielectric constant also showed a positive correlation with backscattered energy having R2 of 0.54 and 0.48 for VV and VH polarization. With this knowledge, surface soil moisture is modeled over bare fields and mapped. Soil moisture modeled is validated using field data, which has R2 of 0.88 and RMSE of 1.93. The developed model and surface soil moisture map are helpful in regional hydrological studies and crop water requirement assessment. © 2021, Società Italiana di Fotogrammetria e Topografia (SIFET).Item Evaluation of surface soil moisture models over heterogeneous agricultural plots using L-band SAR observations(Taylor and Francis Ltd., 2022) Gururaj, P.; Umesh, P.; Shetty, A.The goal of this study is to evaluate the efficiency of surface soil moisture models based on L-band SAR data at two different crop stages in typical Indian agricultural plots. Agricultural fields examined include paddy, tomato, sugarcane, at two distinct crop stages, and a reference fallow field. Among the evaluated models, X-Bragg model underestimates soil moisture in all agricultural fields, whereas the Oh 2004 model fits into three agricultural plots for two crop stages without any necessity of auxiliary field information. All models underperformed in the case of sugarcane at the grand growth stage. Although WCM gave best result, it came at the cost of field data utilized to calibrate model parameters. Overall, the Oh 2004 model outperforms other models across crop types and growth stages. To the best of our knowledge, this is the only study that deals with soil moisture estimations at the plot scale across different crops. © 2022 Informa UK Limited, trading as Taylor & Francis Group.Item Vertical accuracy assessment of open source digital elevation models under varying elevation and land cover in Western Ghats of India(Springer Science and Business Media Deutschland GmbH, 2022) Shetty, S.; Vaishnavi, P.C.; Umesh, P.; Shetty, A.The selection of suitable DEM from available open-source DEMs like SRTM, ALOS World 3D, CARTOSAT-1, ASTER-GDEM, TanDEM-X which are acquired through different techniques is difficult without prior guidelines, especially on the rugged mountainous terrain. Therefore, this article aimed to evaluate the role of land cover and altitude on the vertical accuracy of open-source DEMs with near to ground measurements taken by Ice Cloud and Land Elevation (ICESat) Geoscience Laser Altimetry System (GLAS) in and around Western Ghats (WG) of India. The SRTM (30 m) DEM outperformed other DEMs at the scale of WG and in the dense vegetation cover with least performance by ASTER DEM (30 m). The vertical accuracy of DEM is varying with different elevation ranges and land cover conditions and is found to be better than the vertical accuracy specified by the mission. The overestimation of elevation in low terrain relief area, and underestimation on higher elevation with steep terrain is substantive in all the DEMs. The role of land cover and altitude is significant on the elevation and slope more than the aspect and roughness. Good performance by 90-m resolution DEM over 30-m resolution DEMs proves the potential of InSAR in elevation measurement in vegetated areas with low cost and high accuracy. These results help in the selection of pertinent DEM for any geo-climatical applications and in development of merged DEM based on the terrain relief and land cover of the region. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.Item Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data(Springer, 2022) Keerthana, N.; Salma, S.; Dodamani, B.M.In India, the majority of the population relies heavily on rice as it is their primary source of nutrition. Rice crop yield productivity depends on seasonal variations and mainly depends on hydrological conditions. Long-term water clogging in rice fields for an extended period causes crop flooding and reduces production in terms of quality and quantity. This study deals with the identification of rice crop fields and their flooding due to surface irrigation using Sentinel-1 SAR data. The identification of rice fields was attempted by classifying the image data using a random forest algorithm. For crop flooding analysis, the temporal backscatter of the corresponding fields has been extracted from SAR data and local thresholding is used. The temporal analysis of the SAR backscattering showed a similar tendency in terms of crop growth. The overall accuracy of rice crop classification for VH and VV is 97.30% and 92.24% with RMSE errors of 0.0143 and 0.0145, respectively, obtained at the peak stage of the crop. From the crop flooding analysis, it is observed that crop fields have been flooded at the growth stage due to surface irrigation and rainfall. We identified crop flooding even at the crop mature stage. In the analysis, it has been observed that the flooding is not due to irrigation water but is due to the precipitation water. © 2022, Indian Society of Remote Sensing.Item Restoration and Enhancement of Aerial and Synthetic Aperture Radar Images Using Generative Deep Image Prior Architecture(Springer Science and Business Media Deutschland GmbH, 2022) Shastry, A.; Smitha, A.; George, S.; Padikkal, J.Restoration and enhancement of low light images is an inevitable pre-processing activity among remote sensing, aerial and satellite imaging modalities. The images captured under various atmospheric conditions are distorted. Therefore, they need a thorough conditioning before being analysed. In this paper, we propose a retinex-based variational framework designed under a generative deep image prior architecture to restore and enhance distorted images from satellite, aerial and remote sensing applications. The model handles data-correlated speckle noise found in active image sensing modalities, duly considering its distribution. The data-fidelity aspect of the proposed variational framework is designed using the Bayesian Maximum A Posteriori (MAP) estimate, assuming that the input images are contaminated with Gamma distributed speckled interference. Further, model is catered to handle various noise distributions, such as Gaussian and Poisson, by appropriately altering the data fidelity term specific to the distribution, without modifying the architecture of the model. The variational retinex model employed herein also addresses contrast degradation and intensity inhomogeneity aberrations in the input images. The proposed model is assessed qualitatively using visual comparisons and quantified using the relevant statistical measures. The experimental results confirm that the proposed model outperforms the existing methods in terms of restoration and contrast enhancement of speckled images. The proposed method also has shown the full potential to adapt the model to restore the degraded images following any distribution. © 2022, Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V.Item An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data(Taylor and Francis Ltd., 2023) Salma, S.; Keerthana, N.; Dodamani, B.M.To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.