摘要
在卫星遥感检测中,云对数据的反演带来了严重的干扰。精确地确定云区及减弱云的干扰是具有挑战性的问题。根据云的大气辐射特性给出了卫星图像亮度变化率计算公式、图像反射率的变化率计算公式和热红外波段图像亮度温度变化率的计算方法。利用单波段亮度变化率检测卫星数据中云干扰相对强度的新方法,快速、精确地获取云区位置,得到云区以外无云干扰的数据。根据各光谱波段具有不同的透射云层能力的特点,文章还给出了卫星各红外波段对云层透射能力的客观评价以及影响透射效果的因素。在此基础上利用红外波段对伪卷云透射图像数据完成了多波段数据融合。很好地恢复了可见光波段云区下面的真实数据。统计数据表明,融合后的云区图像数据与可见光波段相关性保持一致。
In satellite remote-sensing detection, cloud as an interference plays a negative role in data retrieval. How to discern the cloud fields with high fidelity thus comes as a need to the following research. A new method rooting in atmospheric radiation characteristics of cloud layer, in the present paper, presents a sort of solution where single-band brightness variance ratio is used to detect the relative intensity of cloud clutter so as to delineate cloud field rapidly and exactly, and the formulae of brightness variance ratio of satellite image, image reflectance variance ratio, and brightness temperature variance ratio of thermal infrared image are also given to enable cloud elimination to produce data free from cloud interference. According to the variance of the penetrating capability for different spectra bands, an objective evaluation is done on cloud penetration of them with the factors that in fluence penetration effect. Finally, a multi-band data fusion task is completed using the image data of infrared penetration from cirrus nothus. Image data reconstruction is of good quality and exactitude to show the real data of visible band covered by cloud fields. Statistics indicates the consistency of waveband relativity with image data after the data fusion.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2006年第11期2011-2015,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金(10274080)资助项目
关键词
亮度变化率
云检测
数据融合
Brightness variance ratio
Cloud detection
Data fusion
