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Remote sensing image mosaicking is an essential processing step in generating large area coverage map using multi-temporal image scenes/strips. The mosaic data is useful for various space-borne applications that span across national level crop assessment, wetland monitoring, and snow and glacier studies, to derive important environmental indicators for sustainable development. This article highlights a novel image mosaicking processing workflow that ingests input geo-referenced image strips with sufficient overlap in-between, and generates country-level mosaic data product. The procedure takes care of large-sized geo-referenced image’s handling and re-projection, and makes data ready for mosaic processing. We have developed strip geo-registration method using Scale Invariant Feature Transform (SIFT) and Mode Biased Random Sample Consensus (MB-RANSAC) outlier removal technique to achieve sub-pixel registration accuracy. Image stitching workflow ingests co-registered image strips, and performs overlap extraction, seamline detection using multi-frame joint strategy, and image blending using region-based statistics in an automatic manner. The mosaic system has been evaluated with Resourcesat’s medium resolution optical remote sensing images over Indian subcontinent, and it has been confirmed that the common region among image strips attains required radiometric and geometric fidelity after correction. It also has been found that the average spectra deviation is less than 0.127% at different classes. |
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