Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.

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Water is one of the primary requirements of any region for sustainable economic development. There are number of limitations regarding availability of surface and subsurface water due to various reasons, hence exploration of groundwater becomes inevitable. Main objective of this study was to map groundwater potential zones for study area using geospatial tools; which comprises of watersheds in Upper Bhima Basin, Pune district. The primary groundwater controlling factors considered are geomorphology, soil, land use land cover, slope, drainage density and lineament density; for which respective maps were prepared using satellite image, toposheets and incidental data. Maps for various layers according to above said controlling factors were generated from different data collected. Finally these thematic layers were integrated using ArcGIS software to prepare groundwater potential zone map for the study area. Groundwater potential zones were marked as ‘very poor, ‘poor’, ‘moderate’, ‘good’ and ‘very good’, based on knowledge based weightage factor. This, geo-spatial techniques based, result was validated using field data collected from the study area. It is concluded that using geospatial tool, identification and mapping for groundwater potential zones become comparatively easy task with saving lot of time and cost and with greater accuracy.