An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

被引:4
|
作者
Gao, Tan [1 ,2 ,3 ]
Zheng, Liangliang [1 ,3 ]
Xu, Wei [1 ,3 ]
Piao, Yongjie [1 ,3 ]
Feng, Rupeng [1 ,3 ]
Chen, Xiaolong [1 ,2 ,3 ]
Zhou, Tichao [1 ,3 ]
机构
[1] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100039, Peoples R China
[3] Chinese Acad Sci, Key Lab Space Based Dynam & Rapid Opt Imaging Tec, Changchun 130033, Peoples R China
基金
中国国家自然科学基金;
关键词
exposure time; two-dimensional entropy; threshold; cubic spline; image details;
D O I
10.3390/s21103306
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The improper setting of exposure time for the space camera will cause serious image quality degradation (overexposure or underexposure) in the imaging process. In order to solve the problem of insufficient utilization of the camera's dynamic range to obtain high-quality original images, an automatic exposure method for plane array remote sensing images based on two-dimensional entropy is proposed. First, a two-dimensional entropy-based image exposure quality evaluation model is proposed. The two-dimensional entropy matrix of the image is partitioned to distinguish the saturated areas (region of overexposure and underexposure) and the unsaturated areas (region of propitious exposure) from the original image. The ratio of the saturated area is used as an evaluating indicator of image exposure quality, which is more sensitive to the brightness, edges, information volume, and signal-to-noise ratio of the image. Then, the cubic spline interpolation method is applied to fit the exposure quality curve to efficiently improve the camera's exposure accuracy. A series of experiments have been carried out for different targets in different environments using the existing imaging system to verify the superiority and robustness of the proposed method. Compared with the conventional automatic exposure method, the signal-to-noise ratio of the image obtained by the proposed algorithm is increased by at least 1.6730 dB, and the number of saturated pixels is reduced to at least 2.568%. The method is significant to improve the on-orbit autonomous operating capability and on-orbit application efficiency of space camera.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] TWO-DIMENSIONAL NEURAL NETWORK ENTROPY FOR REMOTE SENSING IMAGE ANALYSIS
    Velichko, Andrei
    Wagner, Matthias P.
    Taravat, Alireza
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 1952 - 1954
  • [2] An image segmentation method based on two-dimensional entropy and variance
    Xue, Juntao
    Liu, Zhengguang
    Che, Xiuge
    FOURTH INTERNATIONAL CONFERENCE ON PHOTONICS AND IMAGING IN BIOLOGY AND MEDICINE, PTS 1 AND 2, 2006, 6047
  • [3] A two-dimensional image segmentation method based on genetic algorithm and entropy
    Abdel-Khalek, S.
    Ben Ishak, Anis
    Omer, Osama A.
    Obada, A. -S. F.
    OPTIK, 2017, 131 : 414 - 422
  • [4] Remote sensing images segmentation of rivers based on two-dimensional reciprocal gray entropy
    Wu, Yiquan
    Meng, Tianliang
    Wu, Shihua
    Lu, Wenping
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2014, 42 (12): : 70 - 74
  • [5] Forest image processing method based on fuzzy membership and two-dimensional entropy
    College of Engineering and Technology, Northeast Forestry University, Harbin, China
    不详
    不详
    Int. J. Signal Process. Image Process. Pattern Recogn., 1 (95-102):
  • [6] Edge information detection of remote sensing image based on two-dimensional Otsu algorithm
    Fang, Y. (fangyuanmin@126.com), 1600, Binary Information Press, Flat F 8th Floor, Block 3, Tanner Garden, 18 Tanner Road, Hong Kong (10):
  • [7] Directional remote sensing and change detection based on two-dimensional compressive sensing
    Cheng Tao
    Zhu Guo-Bin
    Liu Yu-An
    JOURNAL OF INFRARED AND MILLIMETER WAVES, 2013, 32 (05) : 456 - 461
  • [8] Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane
    Zunino, Luciano
    Ribeiro, Haroldo V.
    CHAOS SOLITONS & FRACTALS, 2016, 91 : 679 - 688
  • [9] Two-Dimensional Array Processing with Compressed Sensing
    Majumdar, Angshul
    Ram, Shobha Sundar
    2014 IEEE RADAR CONFERENCE, 2014, : 417 - 421
  • [10] A Tsallis-entropy Image Thresholding Method Based on Two-dimensional Histogram Obique Segmentation
    Tian, Xiaoguang
    Hou, Xiaorong
    2009 WASE INTERNATIONAL CONFERENCE ON INFORMATION ENGINEERING, ICIE 2009, VOL I, 2009, : 164 - 168