Shirish S. Gedam

Bio: Shirish S. Gedam is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Land use & Total electron content. The author has an hindex of 8, co-authored 27 publications receiving 281 citations. Previous affiliations of Shirish S. Gedam include Indian Institutes of Technology.

GIS based drainage morphometry and its influence on hydrology in parts of Western Ghats region, Maharashtra, India

Abstract: Various drainage morphometric parameters in the Upper Bhima river basin and its influence on hydrological processes (e.g. runoff, peak flow, time to peak, infiltration, overland flow, etc.) were discussed using geographical information system (GIS) and remote sensing techniques. Survey of India topographical maps and ASTER digital elevation model was incorporated for thematic database generation and morphometric parameter evaluation in GIS environment. The whole study basin was divided into 8 sub-basins so that the spatial variation of morphological parameters and its influence on hydrology could be analyzed. The interrelationship between morphometric variables were computed (p < 0.05) and presented in a correlation matrix. The study revealed that mean basin slope (), drainage density (), length of overland flow () and basin relief () are closely associated and significantly related with a large number of morphometric variables. Due to close proximity of Western Ghats, sub-basins 1, 2, 4, 5 and 6 are high...

Monitoring land use changes associated with urbanization: An object based image analysis approach

Abstract: Land use/land cover (LULC) change occurs due to natural and anthropogenic causes In developing countries, rapid industrialization and urbanization imposes a major threat to natural environment Pr

Image Matching Using SIFT Features and Relaxation Labeling Technique—A Constraint Initializing Method for Dense Stereo Matching

Abstract: A probabilistic neural-network-based feature-matching algorithm for a stereo image pair is presented in this paper, which will be useful as a constraint initializing method for further dense matching technique. In this approach, scale-invariant feature transform (SIFT) features are used to detect interest points in a stereo image pair. The descriptor which is associated with each keypoint is based on the histogram of the gradient magnitude and direction of gradients. These descriptors are the preliminary input for the matching algorithm. Using disparity range computed by visual inspection, the search area can be restricted for a given stereo image pair. Reduced search area improves the computation speed. Initial probabilities of matches are assigned to the keypoints which are considered as probable matches from the selected search area by Bayesian reasoning. The probabilities of all such matches are improved iteratively using relaxation labeling technique. Neighboring probable matches are exploited to improve the probability of best match using consistency measures. Confidence measures considering the neighborhood, unicity, and symmetry are some validation techniques which are built into the technique presented here for finding accurate matches. The algorithm is found to be effective in matching SIFT features detected in a stereo image pair with greater accuracy, and these accurate correspondences can be used in finding the fundamental matrix which encodes the epipolar geometry between the given stereo image pair. This fundamental matrix can then be used as a constraint for finding inliers that are used in matching methods for deriving dense disparity map.

Evaluation of GPS Standard Point Positioning with Various Ionospheric Error Mitigation Techniques

Abstract: Abstract The present paper investigates accuracy of single and dual-frequency Global Positioning System (GPS) standard point positioning solutions employing different ionosphere error mitigation techniques. The total electron content (TEC) in the ionosphere is the prominent delay error source in GPS positioning, and its elimination is essential for obtaining a relatively precise positioning solution. The estimated delay error from different ionosphere models and maps, such as Klobuchar model, global ionosphere models, and vertical TEC maps are compared with the locally derived ionosphere error following the ion density and frequency dependence with delay error. Finally, the positional accuracy of the single and dual-frequency GPS point positioning solutions are probed through different ionospheric mitigation methods including exploitation of models, maps, and ionosphere-free linear combinations and removal of higher order ionospheric effects. The results suggest the superiority of global ionosphere maps for single-frequency solution, whereas for the dual-frequency measurement the ionosphere-free linear combination with prior removal of higher-order ionosphere effects from global ionosphere maps and geomagnetic reference fields resulted in improved positioning quality among the chosen mitigation techniques. Conspicuously, the susceptibility of height component to different ionospheric mitigation methods are demonstrated in this study which may assist the users in selecting appropriate technique for precise GPS positioning measurements.

Remote Sensing And Image Interpretation

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Developments in Landsat Land Cover Classification Methods: A Review

Abstract: Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor

Abstract: Image matching based on local invariant features is crucial for many photogrammetric and remote sensing applications such as image registration and image mosaicking. In this paper, a novel local feature descriptor named adaptive binning scale-invariant feature transform (AB-SIFT) for fully automatic remote sensing image matching that is robust to local geometric distortions is proposed. The main idea of the proposed method is an adaptive binning strategy to compute the local feature descriptor. The proposed descriptor is computed on a normalized region defined by an improved version of the prominent Hessian affine feature extraction algorithm called the uniform robust Hessian affine algorithm. Unlike common distribution-based descriptors, the proposed descriptor uses an adaptive histogram quantization strategy for both location and gradient orientations, which is robust and actually resistant to a local viewpoint distortion and extremely increases the discriminability and robustness of the final AB-SIFT descriptor. In addition to the SIFT descriptor, the proposed adaptive quantization strategy can be easily extended for other distribution-based descriptors. Experimental results on both synthetic and real image pairs show that the proposed AB-SIFT matching method is more robust and accurate than state-of-the-art methods, including the SIFT, DAISY, the gradient location and orientation histogram, the local intensity order pattern, and the binary robust invariant scale keypoint.

Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP)

Abstract: In the present study, an effort was made in Kashmir Valley, NW-Himalayas to delineate potential groundwater areas using GIS and Analytical Hierarchy method (AHP) by incorporating remote sensing dat...

Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data

Abstract: Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization.