HDR Synthesis Technology for Spaceborne CMOS Cameras Based on Virtual Digital TDI

Due to the fact that the traditional high dynamic range (HDR) imaging methods cannot be used for satellites, finding a way to generate HDR remote sensing images from the satellites has long been explored in the field of remote sensing imaging. In this article, a systematic method of synthesizing the HDR remote sensing images based on a new registration algorithm and virtual digital delay integration (TDI) technology is proposed. First, a series of original images are generated by the fast continuous shooting method through a push-broom spaceborne camera. Then, a new registration algorithm with high accuracy and high robustness is proposed in this article, which is used for image registration. Finally, an HDR multiframe image synthesis algorithm is used to generate high-quality and high signal-to-noise-ratio HDR images. This technology greatly improves the image information acquisition capabilities of digital TDI area scan cameras.

[1]  Shuai Yang,et al.  Remote Sensing Image Change Saliency Detection Technology , 2018, Journal of Physics: Conference Series.

[2]  Deng Tao.  Remote Sensing Image Registration Based on SURF Descriptor , 2010 .

[3]  Rong Shu,et al.  The development of Chinese hyperspectral remote sensing technology , 2005, SPIE/COS Photonics Asia.

[4]  Keiji Yanai,et al.  Image region entropy: a measure of "visualness" of web images associated with one concept , 2005, MULTIMEDIA '05.

[5]  Kimmo Roimela,et al.  High dynamic range texture compression , 2006, ACM Trans. Graph..

[6]  Wim Diels,et al.  CMOS long linear array for space application , 2006, Electronic Imaging.

[7]  Manuel Guizar-Sicairos,et al.  Efficient subpixel image registration algorithms. , 2008, Optics letters.

[8]  Hamish Millar.  High dynamic range ("HDR") technology , 2006, COMG.

[9]  Chen Gui-lan.  Application of improved RANSAC algorithm to image registration , 2010 .

[10]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[11]  Sankar K. Pal,et al.  Entropy: a new definition and its applications , 1991, IEEE Trans. Syst. Man Cybern..

[12]  Junlin Li,et al.  A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS , 2017 .

[13]  G. Meynants,et al.  Time-Delay-Integration Architectures in CMOS Image Sensors , 2009, IEEE Transactions on Electron Devices.

[14]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.

[15]  Andreas König,et al.  An HDR CMOS image sensor with spiking pixels, pixel-level ADC, and linear characteristics , 2002 .

[16]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[17]  Liang Huang,et al.  An HDR imaging method with DTDI technology for push-broom cameras , 2018 .

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[19]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[20]  Shih-Fu Chang,et al.  Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[21]  Y. Raghavender Rao,et al.  APPLICATION OF NORMALIZED CROSS CORRELATION TO IMAGE REGISTRATION , 2014 .

[22]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Mark Hedley,et al.  Fast corner detection , 1998, Image Vis. Comput..

[24]  Paul Hickson,et al.  Time-delay integration CCD read-out technique: image deformation , 1992 .

[25]  Xuelong Hu,et al.  Video object matching based on SIFT algorithm , 2008, 2008 International Conference on Neural Networks and Signal Processing.