首页> 外文会议>ISPRS Technical Commission I Symposium >RADIOMETRIC AND GEOMETRIC CHARACTERISTICS OF PLEIADES IMAGES
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RADIOMETRIC AND GEOMETRIC CHARACTERISTICS OF PLEIADES IMAGES

机译:Pleiades图像的辐射和几何特征

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Pleiades images are distributed with 50cm ground sampling distance (GSD) even if the physical resolution for nadir images is just 70cm. By theory this should influence the effective GSD determined by means of point spread function at image edges. Nevertheless by edge enhancement the effective GSD can be improved, but this should cause enlarged image noise. Again image noise can be reduced by image restoration. Finally even optimized image restoration cannot improve the image information from 70cm to 50cm without loss of details, requiring a comparison of Pleiades image details with other very high resolution space images. The image noise has been determined by analysis of the whole images for any sub-area with 5 pixels times 5 pixels. Based on the standard deviation of grey values in the small sub-areas the image noise has been determined by frequency analysis. This leads to realistic results, checked by test targets. On the other hand the visual determination of image noise based on apparently homogenous sub-areas results in too high values because the human eye is not able to identify small grey value differences - it is limited to just approximately 40 grey value steps over the available gray value range, so small difference in grey values cannot be seen, enlarging results of a manual noise determination. A tri-stereo combination of Pleiades 1A in a mountainous, but partially urban, area has been analyzed and compared with images of the same area from WorldView-1, QuickBird and IKONOS. The image restoration of the Pleiades images is very good, so the effective image resolution resulted in a factor 1.0, meaning that the effective resolution corresponds to the nominal resolution of 50cm. This does not correspond to the physical resolution of 70cm, but by edge enhancement the steepness of the grey value profile across the edge can be enlarged, reducing the width of the point spread function. Without additional filtering edge enhancement enlarges the image noise, but the average image noise of approximately 1.0 grey values related to 8bit images is very small, not indicating the edge enhancement and the down sampling of the GSD from 70cm to 50cm. So the direct comparison with the other images has to give the answer if the image quality of Pleiades images is on similar level as corresponding to the nominal resolution. As expected with the image geometry there is no problem. This is the case for all used space images in the test area, where the point identification limits the accuracy of the scene orientation.
机译:Pleiades图像分布在50cm地面采样距离(GSD)中,即使Nadir图像的物理分辨率仅为70厘米。借鉴,这应该影响通过在图像边缘的点扩散函数确定的有效GSD。然而,边缘增强可以提高有效的GSD,但这应该引起放大的图像噪声。通过图像恢复可以减少图像噪声。最后甚至优化的图像恢复不能将图像信息从70cm到50cm改善而不丢失细节,需要与其他非常高分辨率的空间图像进行宠物图像细节的比较。通过对具有5个像素倍数5像素的任何子区域的整个图像进行分析来确定图像噪声。基于小子区域中灰度值的标准偏差,通过频率分析确定了图像噪声。这导致了测试目标检查的现实结果。在另一方面图像的视觉判定噪声基于在过高的值明显同质子区域的结果,因为人眼不能识别的小的灰度值的差异 - 它被限制为只是大约40在可用的灰度的灰度值的步骤值范围,因此无法看到灰度值的小差异,扩大了手动噪声的结果。已经分析了山区,但部分城市,区域,但部分城市,与世界观 - 1,Quickbird和Ikonos的相同区域的图像进行了分析,但部分城市的三立体声组合。 Pleiades图像的图像恢复非常好,因此有效的图像分辨率导致因子1.0,这意味着有效分辨率对应于50cm的标称分辨率。这与70cm的物理分辨率不相对应,但是通过边缘增强可以放大边缘横跨边缘的灰度值轮廓的陡度,从而降低点扩散函数的宽度。没有额外的过滤边缘增强增大图像噪声,但与8位图像相关的大约1.0灰度值的平均图像噪声非常小,而不是指示GSD的边缘增强和从70cm到50cm的下降采样。因此,如果Pleiades图像的图像质量与标称分辨率相对应的相似水平,则与其他图像的直接比较必须给出答案。与图像几何形状一样,没有问题。这是测试区域中所有使用的空间图像的情况,其中点识别限制了场景方向的准确性。

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