Wide area analysis of video image for ground truth of satellite image

卫星图像地面实况视频图像的广域分析

• 批准号: 08650482
• 负责人: MURAMOTO Ken-ichiro
• 金额: $ 1.09万
• 依托单位: Kanazawa University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1996
• 资助国家: 日本
• 起止时间: 1996 至 1997
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08650482/
• 关键词: Antarctica; Sea ice; Video image; Satellite image; Ground truth

Measurement of the seasonal cycle and interannual variations of Antarctic sea ice is important for investigations of global climate and ship navigation. While satellite data give a great amount of information about ice conditions, there still remains a need for in situ validations. Resolution of ground truth reference data is much higher than NOAA satellite data. In order to compare the ground truth data with satellite data, analyzing area of ground truth data have to be expanded by patching the ground based data continuously ; and both need to be analyzed quantitatively.In the video image analysis, a technique for measuring sea ice characteristics over a wide area using video images taken from a ship is proposed. The sea ice was photographed by video camera from the ship. Continuous video images are obtained using geometric transformation and template matching. Both size of the ice and concentration along the ship's route can be obtained continuously.The other hand, remote sensing obs … More ervation from a meteorological satellite offers the best available means to understand polar surface conditions, because of their homogeneity over a wide area. However, in the polar region, cloud, snow and ice have almost the same albedo in the visible channel and the same brightness temperature in the infrared channel. Therefore, it is difficult to distinguish among these regions using only the threshold of gray level of a satellite image. In this work, a method to classify clud, sea ice and ground is proposed. This study is based upon analysis of the NOAA/AVHRR infrared images in Antarctica. The algorithm consists of two major approaches : extraction of image features and a classification algorithm. Minimum distance classifier was used to classify that region into one of three categories using five image features. To improve the classification accuracy, threshold boundaries for minimum distance classifier were changed. Both classified and misclassfied areas were decreased with increasing the threshold levels. In geneal, the error rates will be decided according to the purpose.Since both methods of anarysis of sea ice characteristics using video image and extraction of sea ice area using satellite data, it is useful for exanining tke correlation of these two data. Less

南极海冰的季节变化和年际变化对全球气候研究和船舶航行具有重要意义。虽然卫星数据提供了大量有关冰情的信息，但仍需要进行现场验证。地面实况参考数据的分辨率远高于NOAA卫星数据。为了将地面实况数据与卫星数据进行比较，需要不断地对地面实况数据进行拼接，扩大地面实况数据的分析范围，并对两者进行定量分析，在视频图像分析中，提出了一种利用船舶拍摄的视频图像测量大范围海冰特征的方法。海冰被船上的摄像机拍了下来。通过几何变换和模板匹配获得连续视频图像。另外，还可以利用遥感技术，对海冰的大小和沿着海冰的浓度进行连续的观测， ...更多信息 气象卫星的观测提供了了解极地表面状况的最佳手段，因为它们在广阔的区域内具有同质性。而在极区，云、雪、冰在可见光通道中具有几乎相同的偏振度，在红外通道中具有几乎相同的亮温。因此，仅使用卫星图像的灰度阈值难以区分这些区域。在这项工作中，提出了一种方法来分类clud，海冰和地面。本研究是基于对南极地区NOAA/AVHRR红外图像的分析。该算法包括两个主要的方法：提取图像特征和分类算法。最小距离分类器被用于使用五个图像特征将该区域分类为三个类别中的一个。为了提高分类精度，改变了最小距离分类器的阈值边界。分类和误分类的面积都随着阈值水平的增加而减少。一般情况下，误差率将根据目的而定，由于利用视频图像分析海冰特征和利用卫星数据提取海冰面积的方法都有助于检验这两种数据的相关性。少

项目成果

K.Muramoto, T.Yamanouchi: "Classification of polar satellite data using image features and decision tree classifier" Proc.NIPR Symp.Polar,Meteorol.Glaciol.10. 127-137 (1996)

K.Muramoto、T.Yamanouchi：“使用图像特征和决策树分类器对极地卫星数据进行分类”Proc.NIPR Symp.Polar,Meteorol.Glaciol.10。

K.Muramoto, H.Saito: "Could and ice detection using NOAA/AVHRR data" Int.Geosci.Remote Sensing Symp.73-75 (1997)

K.Muramoto、H.Saito：“使用 NOAA/AVHRR 数据进行云和冰检测”Int.Geosci.Remote Sensing Symp.73-75 (1997)

K.Muramoto, T.Endoh: "Sea ice concentration and floe size distribution in the Antarctic using video image processing" Int.Geosci.Remote Sensing Symp.414-416 (1997)

K.Muramoto、T.Endoh：“使用视频图像处理处理南极海冰浓度和浮冰尺寸分布”Int.Geosci.Remote Sensing Symp.414-416 (1997)

K.Muramoto, T.Yamanouchi: "Classification of polar satellite data using image features and decision tree classifier" Proc.NIPR Symp.Polar Meteorol.Glaciol.10. 127-137 (1996)

K.Muramoto、T.Yamanouchi：“使用图像特征和决策树分类器对极地卫星数据进行分类”Proc.NIPR Symp.Polar Meteorol.Glaciol.10。

K.Muramoto, M.Kubo: "Classification of polar satellite data using minimum distance method" Proc.Int.Symp.Environ.Res.Antarctic.(in print). (1998)

K.Muramoto、M.Kubo：“使用最小距离方法对极地卫星数据进行分类”Proc.Int.Symp.Environ.Res.Antarctic。（印刷中）。