Image acquisition in a wider swath, cannot assess the best spatial resolution (SR) and temporal resolution (TR) simultaneously, due to inherent limitations of space borne sensors. But any of the information extraction from remote sensed (RS) images demands the above characteristics. As this is not possible onboard, suitable ground processing techniques need to be evolved to realise the requirements through advanced image processing techniques. The proposed work deals with processing of two onboard sensor data viz., Resourcesat-1 (RS1): LISS-III, which has medium swath combined with AWiFS, which has wider swath data to provide high spatial and temporal resolution at the same instant. LISS-III at 23m and 24 days, AWiFS at 56m and 5 days spatial and temporal revisits acquire the data at different swaths. In the process of acquisition at the same time, the 140km swath of LISS-III coincides at the exact centre line 740km swath of AWiFS. If the non-overlapping area of AWiFS has same features of earth’s surface as of LISS-III overlapping area, it then provides a way to increase the SR of AWiFS to SR of LISS-III in the same non-overlapping area. Using this knowledge, a novel processing technique Fast One Pair Learning and Prediction (FOPLP) is developed in which time is optimized against the existing methods. FOPLP improves the SR of LISS-III in non-overlapping area using technique Single Image Super Resolution (SISR) with Non Sub sampled Contourlet Transforms (NSCT) method and is applied on different sets of images. The proposed technique resulting into an image having TR of 5 days, 740km swath at SR of 23m. Results have shown the strength of the proposed method in terms of computation time and prediction accuracy assessment.
Improving GPR Imaging of the Buried Water Utility Infrastructure by Integrating the Multidimensional Nonlinear Data Decomposition Technique into the Edge Detection