基于场景的单应性变换非均匀性校正
Scene-based nonuniformity correction with homography transformation
AI总结：
本研究针对无人机热成像中UC-FPAs的增益和偏置漂移问题，提出基于单应性变换的场景非均匀性校正方法，利用平移图像联合估计热值与参数，恢复误差小于0.01摄氏度。
摘要：
由于成本低、质量轻且尺寸小，基于非制冷微测辐射热计的热焦平面阵列（UC-FPAs）在长波红外（LWIR）成像应用中非常有用。然而，在农业遥感等典型的户外环境中，基于UC-FPAs的相机可能会出现偏置和增益的漂移。为了解决这种持续漂移，系统需要持续校准。本研究的目标是通过一种计算方案来消除这一需求。在先前的研究中，我们通过在两个不同模糊级别下拍摄的一系列连续图像对，估计了未知的增益和偏置值以及物体的热图像。在当前的工作中，我们利用一系列平移图像来解决类似问题，其中由无人机悬停引起的相对平移通过单应性变换进行建模。这将我们的工作置于基于场景的非均匀性校正问题领域。我们表明，物体的热值以及增益和偏置可以通过依赖几组平移图像进行联合估计。我们使用最小似然估计器，通过交替最小化求解。配准采用广义Lucas-Kanade方法。模拟显示了有前景的精度，真值与恢复图像之间的平均Pearson相关系数超过0.9999998。在理想假设下，这相当于平均恢复误差小于0.01摄氏度。
英文摘要：
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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