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Automatic Registration of INSAT-3D Daily Images Using Mutual Information and Stochastic Optimization Technique

DOI:10.1007/s12524-018-0820-0 期刊:Journal of the Indian Society of Remote Sensing 出版年份:2018 更新时间:2025-09-23 15:21:21
摘要: An automatic image registration approach is presented here can be used to register daily images of Indian geostationary satellite system INSAT-3D acquired every 30 min’ interval without use of any ground control points (GCPs). There is always a pressing need to register meteorological images that are acquired over earth from geostationary platforms every 15–30 min, covering almost one-third of the earth. Weather forecast activities include derivation of atmospheric motion vectors, which demand immediate processing of such images to a reasonable accuracy in terms of its relative location accuracy. Generally followed approaches make use of image navigation models and GCPs drawn from known landmarks in land ocean boundaries and correlate image features before estimating a transform to warp the current acquisition to a known geometry. However, the hierarchical (coarse to ?ne) approach explained here makes use of intensity based Mutual Information as a similarity measure from a population of pixels selected randomly and uses stochastic gradient descent optimizer to estimate an af?ne transform between registering image pair, delivers satisfactory results.
作者: Manthira Moorthi Subbiah,Debajyoti Dhar,Sivakumar Ramamoorthy
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To present an automatic image registration approach for registering daily images of the Indian geostationary satellite system INSAT-3D without the use of ground control points (GCPs), focusing on meteorological images acquired every 15–30 minutes.

The automatic INSAT-3D channel data registration method, using MI and gradient descent optimization, achieves registration accuracies within ± 0.5 resolution units, even in the presence of clouds and low contrast. This approach is suitable for real-time processing chains and does not require preprocessing steps or invariant feature sets.

The study acknowledges the challenges of registering meteorological images due to cloud presence, low contrast, and imaging in night time. The method's robustness and accuracy are tested under these conditions, but the paper does not explicitly discuss limitations beyond these inherent challenges.

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