基于薄云雾去除的ETM+影像大气校正

Atmospheric correction for ETM+ image based on thin cloud removal

中国南方农业遥感监测中,遥感影像常常受到薄云雾影响,大气的散射与吸收作用会使传感器接收到的地物反射率与真实值之间存在差距,是导致数据质量下降的主要原因,薄云雾去除和大气校正处理是十分必要的。该研究利用LandSat-7/ETM+影像,结合背景抑制云雾厚度因子(BSHTI)云检测方法和虚拟云点(VCP)云去除方法进行薄云雾去除,并与暗元法去云处理结果对比分析,然后将去云处理后的影像进行FLAASH大气校正,选取校正前后典型地物的光谱特征和NDVI值进行分析评价。结果表明,BSHTI-VCP法可有效消除薄云雾对遥感数据的影响,提高了云雾覆盖范围的影像质量;FLAASH大气校正较好地消除了大气影响,获得了地物真实地表反射率。该研究为南方作物遥感监测中定量反演与信息解译提供了良好理论支持。

When processed agricultural remote sensing monitoring in the south of China, remote sensing images are often affected by thin cloud. Atmospheric absorption and scattering effect can make the sensor receives ground reflectance differs from the true value, which is the main reason for the decline of remote sensing data. It is necessary to remove thin cloud and fog and make the atmospheric correction. This study used the LandSat-7/ETM＋ image, made the cloud removal by BSHTI-VCP method, contrast the result by dark element method, made the FLAASH atmospheric correction with the processed image, analyzed and evaluated the spectral characteristics and NDVI value of typical objects before and after correction. The results showed that the BSHTI-VCP method can lower pixel gray value of visible light and near-infrared wave with 0.3341-0.5476 and 0.0591-0.2512, separately, 0.0529-1.0729 increase in image average gradient, and raise information entropy, too. The BSHTI-VCP method can effectively eliminate the influence of thin cloud and fog on remote sensing data which increased the image quality of cloud cover range. FLAASH atmospheric correction effectively eliminates the effect of atmosphere and can obtain the ground truth surface reflectance, improve the spectral characters of cropland surface, obviously. This study provides theoretical basis for crops remote sensing monitoring further quantitative inversion and information interpretation in the south of China.