Self-Calibration Method Based on Surface Micromaching of Light Transceiver Focal Plane for Optical Camera

2018

Opt. Express

Self Cite

Internal parameter calibration of remote sensing cameras (RSCs) is a necessary step in remote sensing photogrammetry. On-orbit camera calibration widely adopts external ground control points (GCPs) to measure its internal parameters. However, accessible and available GCPs are not easy to achieve when cameras work on a satellite platform. In this paper, we propose an efficient camera self-calibration method using a micro-transceiver in conjunction with deep learning. A supervised learning set is produced by the micro-transceiver, where multiple two-dimensional diffraction grids are produced and transformed into multiple auto-collimating sub-beams equivalent to infinite target-point training examples. A deep learning network is used to invert the learnable internal parameters. The micro-transceiver can be easily integrated into the internal structure of RSCs allowing to calibrate them independently on external ground control targets.

Section: Introductionmentioning

confidence: 99%

Efficient camera self-calibration method for remote sensing photogrammetry

2018

Opt. Express

Self Cite

“…Remote sensing integrated camera (RSIC) technologies have become a new focus of research in small- and microsatellite photography 1 – 3 . Compressive sampling (CS) is considered the holy grail of RSIC technologies because it can improve the degree of integration of camera systems, compression systems, high-capacity storage systems, and high-speed image transmission systems.…”

Section: Introductionmentioning

confidence: 99%

Compressive sampling based on frequency saliency for remote sensing imaging

2017

Sci Rep

Self Cite

In saliency-based compressive sampling (CS) for remote sensing image signals, the saliency information of images is used to allocate more sensing resources to salient regions than to non-salient regions. However, the pulsed cosine transform method can generate large errors in the calculation of saliency information because it uses only the signs of the coefficients of the discrete cosine transform for low-resolution images. In addition, the reconstructed images can exhibit blocking effects because blocks are used as the processing units in CS. In this work, we propose a post-transform frequency saliency CS method that utilizes transformed post-wavelet coefficients to calculate the frequency saliency information of images in the post-wavelet domain. Specifically, the wavelet coefficients are treated as the pixels of a block-wise megapixel sensor. Experiments indicate that the proposed method yields better-quality images and outperforms conventional saliency-based methods in three aspects: peak signal-to-noise ratio, mean structural similarity index, and visual information fidelity.

“…Knowledge of the inner orientation parameters (IOPs) of an optical camera, such as its principal distance and principal point, is a prerequisite of image positioning [1][2][3][4] . The accuracy of camera calibration strongly influences the accuracy in determining image position.…”

Section: Introductionmentioning

confidence: 99%

Optical focal plane based on MEMS light lead-in for geometric camera calibration

2017

Microsyst Nanoeng

Self Cite

The focal plane of a collimator used for the geometric calibration of an optical camera is a key element in the calibration process. The traditional focal plane of the collimator has only a single aperture light lead-in, resulting in a relatively unreliable calibration accuracy. Here we demonstrate a multi-aperture micro-electro-mechanical system (MEMS) light lead-in device that is located at the optical focal plane of the collimator used to calibrate the geometric distortion in cameras. Without additional volume or power consumption, the random errors of this calibration system are decreased by the multi-image matrix. With this new construction and a method for implementing the system, the reliability of high-accuracy calibration of optical cameras is guaranteed.