摘要
在卫星上提取图像目标区域是降低传输数据量、提高空间传输链路利用率的重要一环。本文从小波系数相关性的角度,提出一种针对空间图像压缩算法(CCSDS-IDC)的目标区域提取方法,是以图像小波系数LL3子带为起点,以四叉树分解为基础的自适应非均匀矩形分割与合并(ANURSM),将对应背景区域的小波系数与对应目标区域分离。经试验表明,算法能够准确、有效地提取出图像目标区域对应的小波系数,与改进的CCSDS-IDC相结合能够有效降低传输数据量并提高图像目标区域质量。
High-resolution remote sensors and equipments collected far more images than transmitted through limited-band downlink channels. There is a critical need for special target detection algorithm to select subset of the image collection effectively. An adaptive non-uniform rectangular segmentation and merging algorithm（ANURSM） is proposed to meet the requirement. The algorithm extracts coefficients corresponding to target-area from background in LL3 subband of wavelet-transformed images, and can be integrated with CCSDS-IDC（The Consultative Committee for Space Data Systems-Image Data Compression Recommended Standard） easily and seamlessly. Experiments have demonstrated that the algorithm can effectively reduce data volume and improve the quality of target areas in images.
出处
《光电工程》
CAS
CSCD
北大核心
2015年第4期25-31,共7页
Opto-Electronic Engineering
关键词
小波系数相关性
空间图像压缩推荐标准
数据结构
目标区域
矩形分割
dependence of transformed wavelet coefficients
CCSDS-IDC
data structure
target area
rectangular segmentation
