珞珈三号01星冰雪/云场景影像偏色校正处理

珞珈三号01星采用Bayer成像模式,其获取的绿色通道信号相对较强,但由于相机通常采用固定的曝光与增益设置,导致珞珈三号01星拍摄的含冰雪/云覆盖影像会出现严重偏绿现象。针对该问题,提出了一种结合颜色通道补偿与直方图间互相关关系的直方图重叠白平衡方法。为提升影像的细节信息,通过低灰度值区域的分布比例构建伽马函数,实现拉伸处理;为提升各颜色通道分布的相似度,使用影像的标准差信息计算权重,补偿衰减通道,并基于YCbCr颜色空间保持亮度信息,避免影像背景亮度变化;为改善偏色情况并保持色度均值的重合,通过互相关关系进行色度直方图重叠处理,根据冰雪/云场景影像的特征自适应调整参数。对校正后的结果影像进行主观分析与客观评价,并与多种主流的颜色校正方法进行对比验证。实验结果表明,所提方法颜色校正效果更好、影像地物颜色更自然,且能更好地保留地物信息。

Support Vector Machines for Cloud Detection over Ice-Snow Areas

In polar regions, cloud and underlying ice-snow areas are difficult to distinguish in satellite images because of their high albedo in the visible band and low surface temperature of ice-snow areas in the infrared band. A cloud detection method over ice-snow covered areas in Antarctica is presented. On account of different texture features of cloud and ice-snow areas, five texture features are extracted based on GLCM. Nonlinear SVM is then used to obtain the optimal classification hyperplane from training data. The experiment results indicate that this algorithm performs well in cloud detection in Antarctica, especially for thin cirrus detection. Furthermore, when images are resampled to a quarter or 1/16 of the full size, cloud percentages are still at the same level, while the processing time decreases exponentially.

基于随机森林的遥感影像雪冰云信息检测方法

结合随机森林(random forest,RF),提出了一种检测遥感影像雪冰云信息方法。建立有效区分影像中冰凌、雪、云的特征组合,采用随机森林算法和形态学操作得到初步分类检测结果,进一步对分类检测流程优化,增加了“二次检测”。实验结果表明,采用优化后的算法总体分类精度均为90%以上、Kappa系数均为0.8以上,精度较高。该算法说明了采用随机森林算法检测卫星影像的冰凌、雪、云信息的可行性,具有自动化、高精度、高效率监测冰凌灾害和雪冰灾害的优势。

木厂沟:我看见,我征服

在上孟乡寨子的向家接待站住了两天,清晨起来看看周围的青山绿水,感觉已经融入了这里,早就听沈尤说过木厂沟里的米汤海子了,再加上希尔基多雪山的诱惑,我坚定了穿越木厂沟的信念。虽然天上在下雨,也没能改变我们的计划,九点多种,我和杂志社的明可,夏天,好友沈尤,当地的朋友尼玛、向江、阿强和江豹,带着四条猎狗和家当细软坐上了进沟的手扶拖拉机。