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• W763225409 abstract "This research examines the utility of an automated image registration technique that utilizes the Laplacian of Gaussian filter to extract semi-invariant ground control points, the use of matrix transformations for efficient management of affine image relationships, and wavelet theory for multi-resolution analysis. Additionally, advances in both composite and predictive transformations will be covered. Results will be presented that demonstrate the utility of these techniques for processing large data sets such as HyVista Corporation’s HyMap sensor. Automation techniques will be highlighted, demonstrating the strengths and weaknesses when applied to images with high degrees of parallax, cloud-cover, and other types of temporal changes. INTRODUCTION With the rapid advancement of both hyperspectral and hypertemporal imaging capabilities, the need for automated registration of image bands and frames with each other and with an ever-growing database of related images is critical. Similarly, for lowlight conditions such as that encountered in astronomical imaging, analysts are often producing long-dwell composite images by utilizing long integration times or by “stacking” several individual images. These techniques all require precise registration of images, whether it is for change detection, spectral unmixing, or to maximize the signalto-noise ratio of the output image. This registration process can be very slow and tedious when done using manually selected ground control points (GCPs), where an analyst chooses similar reference locations within multiple images and generates the mapping transformation operation necessary for registration. This effort attempts to add automation to this registration process through the use of spatial frequency analysis, edge filtering, point matching, and statistical analysis. This registration technique utilizes comparison of semi-invariant features (edge detail) within a scene to correlate images/spectral bands. With the ever-increasing computational processing speeds available and the continuing sophistication of edge detection/filtering techniques (Gonzalez, 2001), point matching (Chandrasekhar, 1999), and statistical analysis, it is possible to fully automate this task. As is often the case, it is desirable to register high-resolution panchromatic images with lower-resolution multispectral images. If this can be accomplished, it is possible to allow the strengths of each sensor to compensate for the inherent weaknesses of the other, so that analysts can efficiently exploit the spatial and spectral characteristics of the fused data simultaneously (Schott, 1997). Correction for the basic geometric distortions such as shift, rotation, and scale between images will be covered in detail. Wavelet analysis will be utilized to decompose higher resolution images to the equivalent frequency content of a lower resolution image. This will allow automated registration of multi-sensor images utilizing the Laplacian of Gaussian (LoG) filter and automatic point matching techniques. The capabilities of this LoG Wavelet Registration (LoGWaR) technique will be demonstrated on both test data and real multi-sensor data sets." @default.
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• W763225409 date "2006-01-01" @default.
• W763225409 modified "2023-09-23" @default.
• W763225409 title "Automatic tie point selection using Laplacian of Gaussian (LoG) spatial filtering" @default.
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