Frequency selection rule for the HDHF Lissajous scanning imaging with a low-voltage one axis actuated PZT scanner based on an asymmetric fiber cantilever.

Lissajous micro scanners are very attractive in compact laser scanning applications for biomedical endoscopic imaging, such as confocal microscopy, endomicroscopy or optical coherence tomography. The scanning frequencies have a very important effect on the quality of the resulting Lissajous scanning imaging. In this paper, we propose a frequency selection rule for high definition and high frame-rate (HDHF) Lissajous scanning imaging, by deriving the relationship among the scanning field of view (FOV), actuation frequencies and pixel size based on the characteristics of the scanning trajectory. The minimum sampling rate based on the proposed frequency selection rule is further discussed. We report a lead zirconate titanate piezoelectric ceramic (PZT) based Lissajous fiber scanner to achieve HDHF Lissajous scanning imaging. Based on the frequency selection rule, different frequency combinations are calculated, under which the Lissajous fiber scanner can work at the frame rate (FR) of 10 Hz, 20 Hz, 40 Hz and 52 Hz. The trajectory evolution of the Lissajous scanning at the frame rate of 10 Hz has been obtained to verify the applicability of the proposed rule. The measured resolution of the scanner is 50.8 lp/mm at the unit optical magnification, and the measured FOV at the FR of 10 Hz and 40 Hz are 1.620 mm ×1.095 mm and 0.405 mm ×0.27 mm, respectively. HDHF Lissajous scanning images of the customized spatial varying binary pattern are obtained and reconstructed at the FR of 10 Hz and 40 Hz, demonstrating the practicability of the frequency selection rule.

A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images.

Cloud detection is a crucial step in the optical satellite image processing pipeline for Earth observation. Clouds in optical remote sensing images seriously affect the visibility of the background and greatly reduce the usability of images for land applications. Traditional methods based on thresholding, multi-temporal or multi-spectral information are often specific to a particular satellite sensor. Convolutional Neural Networks for cloud detection often require labeled cloud masks for training that are very time-consuming and expensive to obtain. To overcome these challenges, this paper presents a hybrid cloud detection method based on the synergistic combination of generative adversarial networks (GAN) and a physics-based cloud distortion model (CDM). The proposed weakly-supervised GAN-CDM method (available online https://github.com/Neooolee/GANCDM) only requires patch-level labels for training, and can produce cloud masks at pixel-level in both training and testing stages. GAN-CDM is trained on a new globally distributed Landsat 8 dataset (WHUL8-CDb, available online doi:https://doi.org/10.5281/zenodo.6420027) including image blocks and corresponding block-level labels. Experimental results show that the proposed GAN-CDM method trained on Landsat 8 image blocks achieves much higher cloud detection accuracy than baseline deep learning-based methods, not only in Landsat 8 images (L8 Biome dataset, 90.20% versus 72.09%) but also in Sentinel-2 images ("S2 Cloud Mask Catalogue" dataset, 92.54% versus 77.00%). This suggests that the proposed method provides accurate cloud detection in Landsat images, has good transferability to Sentinel-2 images, and can quickly be adapted for different optical satellite sensors.

A Stellar Imaging Error Correction Method Based on an Ellipsoid Model: Taking Ziyuan 3-02 Satellite Data Analysis as an Example.

Stellar point image coordinates are one of the important observations needed for high-precision space attitude measurement with a star sensor. High-coupling imaging errors occur under dynamic imaging conditions. Using the results of preliminary star point extraction from star sensor imaging data combined with a superimposed time series, we analyze the relative motion and trajectory based on the star point image, establish an image error ellipsoid fitting model based on the elliptical orbit of a satellite platform, and achieve geometric error correction of a star sensors' image star point using multi-parameter screening of the ambiguous solutions of intersection of the elliptic equations. The simulation data showed that the accuracy of the correction error of this method reached 89.8%, and every star point coordinate required 0.259 s to calculate, on average. In addition, it was applied to real data from the satellite Ziyuan 3-02 to carry out the correction of the star points. The experiment shows that the mean of attitude quaternion errors for all its components was reduced by 52.3%. Our results show that the estimation parameters of dynamic imaging errors can effectively compensate for the star point image observation value and improve the accuracy of attitude calculation.

Research on Geometric Calibration of Spaceborne Linear Array Whiskbroom Camera.

The geometric calibration of a spaceborne thermal-infrared camera with a high spatial resolution and wide coverage can set benchmarks for providing an accurate geographical coordinate for the retrieval of land surface temperature. The practice of using linear array whiskbroom Charge-Coupled Device (CCD) arrays to image the Earth can help get thermal-infrared images of a large breadth with high spatial resolutions. Focusing on the whiskbroom characteristics of equal time intervals and unequal angles, the present study proposes a spaceborne linear-array-scanning imaging geometric model, whilst calibrating temporal system parameters and whiskbroom angle parameters. With the help of the YG-14-China's first satellite equipped with thermal-infrared cameras of high spatial resolution-China's Anyang Imaging and Taiyuan Imaging are used to conduct an experiment of geometric calibration and a verification test, respectively. Results have shown that the plane positioning accuracy without ground control points (GCPs) is better than 30 pixels and the plane positioning accuracy with GCPs is better than 1 pixel.

Improved Line Tracing Methods for Removal of Bad Streaks Noise in CCD Line Array Image-A Case Study with GF-1 Images.

Remote sensing images could provide us with tremendous quantities of large-scale information. Noise artifacts (stripes), however, made the images inappropriate for vitalization and batch process. An effective restoration method would make images ready for further analysis. In this paper, a new method is proposed to correct the stripes and bad abnormal pixels in charge-coupled device (CCD) linear array images. The method involved a line tracing method, limiting the location of noise to a rectangular region, and corrected abnormal pixels with the Lagrange polynomial algorithm. The proposed detection and restoration method were applied to Gaofen-1 satellite (GF-1) images, and the performance of this method was evaluated by omission ratio and false detection ratio, which reached 0.6% and 0%, respectively. This method saved 55.9% of the time, compared with traditional method.