Key Laboratory of Precision Opto-Mechatronics Technology Sponsored by Ministry of Education, School of Instrumentation and Opto-Electronics Engineering, Beihang University, Beijing, China.
Appl Spectrosc. 2019 Apr;73(4):454-463. doi: 10.1177/0003702819830776.
Temporally and spatially modulated Fourier transform imaging spectrometers (TSMFTISs) can obtain images and interference information of targets during the data acquisition process for remote sensing. Temporally and spatially modulated Fourier transform imaging spectrometers play an important role in target classification and identification, as the spectrum information of targets can be reconstructed with the theory of Fourier transform spectroscopy. However, the defect pixels absent in the planar array charge-coupled device used in imaging spectrometers have a significant impact on the accuracy of target spectral recovery information, so the preprocessing of bad pixels in remote sensing interference images is indispensable to data processing in TSMFTIS. An adaptive defect pixel correction method based on the weighted least squares support vector machine is introduced in this paper. The principle of TSMFTIS is presented to state the specialty of bad pixels and discuss the limitations of the traditional defect pixel method. Simulations based on the conventional method and the proposed method are performed to obtain bad pixel correction results for TSMFTIS. The algorithm presented in this paper is more efficient and robust. An application of the proposed method is employed.
时频调制傅里叶变换成像光谱仪（TSMFTIS）可在遥感数据采集过程中获取目标的图像和干涉信息。时频调制傅里叶变换成像光谱仪在目标分类和识别中起着重要作用，因为可以根据傅里叶变换光谱学理论重建目标的光谱信息。然而，成像光谱仪中使用的面阵电荷耦合器件中不存在的缺陷像素对目标光谱恢复信息的准确性有重大影响，因此遥感干涉图像中坏像素的预处理对于 TSMFTIS 中的数据处理是必不可少的。本文介绍了一种基于加权最小二乘支持向量机的自适应缺陷像素校正方法。阐述了 TSMFTIS 的原理，说明了坏像素的特殊性，并讨论了传统缺陷像素方法的局限性。基于传统方法和所提出的方法进行了仿真，以获得 TSMFTIS 的坏像素校正结果。本文提出的算法更高效、更稳健。并应用了该方法。
