SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images

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• 作者：Kunal Kumar Rai ; Aparna Rai ; Kanishka Dhar…
• 关键词：Image fusion ; Scale invariant feature transform ; Fast approximate nearest Neighbor
• 刊名：Journal of the Indian Society of Remote Sensing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：45
• 期：1
• 页码：55-65
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth Sciences, general; Remote Sensing/Photogrammetry;
• 出版者：Springer India
• ISSN：0974-3006
• 卷排序：45

文摘

Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient.