Download e-book for kindle: Advanced Remote Sensing : Terrestrial Information Extraction by Shunlin Liang, Xiaowen Li, Jindi Wang

By Shunlin Liang, Xiaowen Li, Jindi Wang

ISBN-10: 0123859557

ISBN-13: 9780123859556

Complex distant Sensing is an application-based reference that gives a unmarried resource of mathematical ideas helpful for distant sensing facts collecting and assimilation. It offers state of the art options for estimating land floor variables from numerous facts forms, together with optical sensors resembling RADAR and LIDAR. Scientists in a couple of diversified fields together with geography, geology, atmospheric technological know-how, environmental technology, planetary technology and ecology may have entry to critically-important data
extraction recommendations and their almost limitless purposes. whereas rigorous sufficient for the main skilled of scientists, the ideas are good designed and built-in, making the book’s content material intuitive, essentially provided, and useful in its implementation.

* finished evaluate of varied sensible tools and algorithms * special description of the foundations and tactics of the state of the art algorithms * Real-world case reports open a number of chapters * greater than 500 full-color figures and tables * Edited through best distant sensing specialists with contributions from authors around the geosciences.

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396 m. 2), the maximum image coordinate offset caused by the curvature of the earth on the edge of the satellite imagery is only 1/4 pixel, which is equivalent to the measurement error of the image-point coordinates and can therefore be ignored. The image-point displacement caused by terrain relief is similar to that caused by the curvature of the earth. 4) > > Zc À Yc 2 3 > > dZ : dy ¼ Àf 2 Z where dx and dy are the image-point displacements caused by the terrain relief, X ¼ a1 ðX À XS Þ þ b1 ðY À YS Þ þ c1 ðZ À ZS Þ; Y ¼ a2 ðX À XS Þ þ b2 ðY À YS Þ þ c2 ðZ À ZS Þ; Z ¼ a3 ðX À XS Þ þ b3 ðY À YS Þ þ c3 ðZ À ZS Þ: In projective imagery, the direction of the image-point displacement caused by terrain relief is away from the principal point, in contrast with its effect on radar imagery.

1 Coupled Device) array; and the projection mode that the user ultimately selects. Toutin (2004a) suggested that the sources of systematic errors that produce geometric distortions of satellite remote-sensing imagery can be classified into two types: errors from the observation devices (the remote-sensing platform, the sensor, and other measuring devices of the imaging system such as gyroscope and astronomical instruments) and errors that arise from the observed objects (such as atmosphere and earth).

Furthermore, the model parameters can be solved easily. The most common empirical geometric processing models include the general polynomial model, the direct linear transformation model, the affine transformation model, and the rational function model (RFM). The RFM is an expansion of the first three models. It is a more general and perfect empirical geometric processing model. Because the IKONOS high-resolution satellite imagery used in the United States employs the RFM to replace the rigorous geometric processing model based on the collinearity condition equation for the geometric processing and object positioning of satellite imagery, the RFM has gained considerable attention, and it has been studied by many scholars.

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Advanced Remote Sensing : Terrestrial Information Extraction and Applications by Shunlin Liang, Xiaowen Li, Jindi Wang


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