Faculty Publications
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Item EXhype: A tool for mineral classification using hyperspectral data(Elsevier B.V., 2017) Adep, R.N.; Shetty, A.; Ramesh, H.Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named ‘EXhype (Expert system for hyperspectral data classification)’ to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals. © 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)Item An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images(Elsevier B.V., 2018) Rishikeshan, C.A.; Ramesh, H.The detection and extraction of water bodies from satellite imagery is very important and useful for several planning and developmental activities such as shoreline identification, mapping riverbank erosion, watershed extraction and water resource management. Popular techniques for water body extraction like those based on the normalized difference water index (NDWI) require reflectance information in the green and near-infrared (NIR) bands of the light spectrum. Moreover, some commonly used approaches may perform differently according to the spatial resolution of the images. In this regard, mathematical morphological (MM) techniques for image processing have been employed for spatial feature extraction as they preserve edges and shapes. This study proposes a flexible MM driven approach which is very effective for the extraction of water bodies from several satellite images with different spatial resolution. MM provides effective tools for processing image objects based on size and shape and is particularly adapted for water bodies that have typically specific spatial characteristics. In greater details, the proposed extraction algorithm preserves the actual size and shape of the water bodies since it is based on morphological operators based on geodesic reconstruction. Moreover, the choice of the filter size (called structural element (SE) in MM) in the proposed algorithm is done dynamically allowing one to retain the most precise results from different set of inputs images of different spatial resolution and swath. The availability of more than one spectral band of satellite imagery is not necessary for the proposed algorithm as it utilizes only a single band for its computation. This makes it convenient to apply in single band imageries obtained from satellites such as Cartosat thereby making the proposed approach effective over commonly used methods. The accuracy assessment was carried out and compared with the maximum likelihood (ML) classifier and methods based on spectral indices. In all the five test datasets, extraction accuracy of the proposed MM approach was significantly higher than that of spectral indices and ML methods. The results drawn from visual and qualitative assessments indicated its capability and efficiency in water body extraction from different satellite images. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)Item Evaluating the effects of forest fire on water balance using fire susceptibility maps(Elsevier B.V., 2020) Venkatesh, K.; Konkathi, K.; Ramesh, H.Sudden and long term changes in the landscape can be attributed to periodic wildfires which, is a cyclic occurrence at Kudremukh national forest in Western Ghats of India. These land-use changes influence the hydrology of landscape, causing disintegration of soil, loss of biodiversity, changes in stream and flooding. To understand and account for these land-use changes, a new approach was implemented by developing fire susceptibility map from topographic, climatic and human-induced factors and validating it with MODIS (Moderate-resolution Imaging Spectro-radiometer) fire points for discretising accuracy. The fire susceptibility map can be used for studying the long-term (year or more) effects of fire on water balance systems. The fire susceptibility map generated for the years 2005 and 2017 was overlaid with MODIS LULC (Land Use Land Cover) for establishing the post-fire scenario whereas MODIS LULC MCD12Q1 (2005 and 2017) was considered as the no-fire scenario to analyse the intensity of the fire and its effect on streamflow and infiltration. These maps along with historical satellite hydro-climatic datasets, were used to assess the effect of forest fire on hydrological parameters using the SWAT (Soil and Water Assessment Tool) model. No-fire and post-fire conditions were established by modifying SCS-CN (Soil Conservation Service-Curve Number) based on previous works of literature to represent the catchment as unburnt and burnt area. The SWAT model was calibrated (2002–2008) and validated (2009–2012) for establishing a baseline scenario. The sensitive parameters obtained from SUFI-2 (Sequential Uncertainty Fitting) algorithm in SWAT-CUP (Calibration and Uncertainty Programs) were used to simulate stream flows till 2017 due to lack of observed streamflow data for the year 2017. It was inferred that the effect of wildfire on flows in recent years (2017) had increased radically when compared to the flows before a decade (2005), diminishing the rate of infiltration and causing the deficit in groundwater to energise. The methodology can further be executed in any forest area for distinguishing fire hazard zones and implementing prior actions in those areas for mitigation of forest fires and maintaining sustainable water balance. © 2019 Elsevier Ltd