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COMPRESSION AND ANALYSIS OF VERY LARGE IMAGERY DATA SETS USING SPATIAL STATISTICS

机译：使用空间统计压缩和分析非常大的图像数据集

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As remote sensing instruments evolve, the size of imagery data sets derived from remote sensing continues to increase. Several satellites currently offer resolution of 1 meter per pixel or better. At this resolution, even a small geographic area leads to a very large data set; 1 square mile, for example, is represented by approximately 2.6 x 10^6 pixels. Many sensors are now multispectral or even hyperspectral, increasing the size of the data set by up to 10^2. Processing images for classification or mapping purposes thus poses an increasing computational challenge. This paper describes the use of spatial statistics to compress the size of large 1- meter imagery data sets. The images were taken over locations in the United States using a CAMIS (Computerized Airborne Multispectral Imaging System) instrument flown in an airplane and registered by trained image analysts. Models of spatial variation are first computed on an entire image, then on subsampled sets of the image. Parameters of the models are used to compress the original image. In some cases it is possible to compress data several orders of magnitude without substantially degrading results of subsequent analysis.
机译：作为遥感仪器的发展，从遥感导出的图像数据集的大小继续增加。几个卫星目前提供每像素1米的分辨率或更好。在这个分辨率下，即使是一个小型地理区域也会导致一个非常大的数据集;例如，1平方英里表示约2.6 x 10 ^ 6像素。许多传感器现在是多光谱甚至高光谱，增加了最多10 ^ 2的数据的大小。因此处理用于分类或映射目的的图像造成了增加的计算挑战。本文介绍了空间统计数据压缩大1米图像数据集的大小。使用在飞机中飞行的CAMIS（计算机化空气多光谱成像系统）仪器拍摄了美国的地点，并通过培训的图像分析师注册。空间变化模型首先在整个图像上计算，然后在图像的映像集上计算。模型的参数用于压缩原始图像。在某些情况下，可以压缩数据几个数量级，而不会显着降低后续分析结果。

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