Scene-based nonuniformity correction with homography transformation

• URL: http://arxiv.org/abs/2503.02487v1
• Date: Tue, 04 Mar 2025 10:50:58 GMT
• Title: Scene-based nonuniformity correction with homography transformation
• Authors: Peretz Yafin, Nir Sochen, Iftach Klapp,
• Abstract summary: Camera-based thermal focal plane arrays (UC-FPAs) are useful for long-wave infrared (LWIR)imaging applications.<n>In outdoor conditions typical in agricultural remote sensing, cameras based on UC-FPAs may suffer from drift in offset and gain.<n>We show that an object's thermographic values, as well as gain and offset, can be jointly estimated by relying on a few sets of shifted images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to their affordable, low mass, and small dimensions, uncooled microbolometer-based thermal focal plane arrays (UC-FPAs) are useful for long-wave infrared (LWIR)imaging applications. However, in outdoor conditions typical in agricultural remote sensing, cameras based on UC-FPAs may suffer from drift in offset and gain. To tackle the persistent drift, the system requires continuous calibration. Our goal in this study was to eliminate this requirement via a computational schema. In a former study, we estimated unknown gain and offset values and thermographic images of an object from a sequence of pairs of successive images taken at two different blur levels.In the current work, we took on a similar problem using a sequence of shifted images, with relative shifts caused by realistic drone hovering modeled by homography transformation. This places our work in the realm of scene-based nonuniformity correction problems. We show that an object's thermographic values, as well as gain and offset, can be jointly estimated by relying on a few sets of shifted images. We use a minimum likelihood estimator, which is found using alternating minimization. Registration is done using a generalized Lucas-Kanade method. Simulations show promising accuracy with mean Pearson correlation of more than 0.9999998 between ground truth and restoration. Under ideal assumptions, this is equivalent to a mean restoration error of less than 0.01 Celsius degree.

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