By Shunlin Liang, Xiaowen Li, Jindi Wang
Complicated distant Sensing is an application-based reference that gives a unmarried resource of mathematical ideas worthy for distant sensing info accumulating and assimilation. It provides state of the art thoughts for estimating land floor variables from quite a few information varieties, together with optical sensors comparable to RADAR and LIDAR. Scientists in a couple of assorted fields together with geography, geology, atmospheric technological know-how, environmental technology, planetary technology and ecology could have entry to critically-important data
extraction concepts and their almost limitless purposes. whereas rigorous adequate for the main skilled of scientists, the recommendations are good designed and built-in, making the book’s content material intuitive, essentially offered, and useful in its implementation.
* entire review of assorted functional equipment and algorithms * distinct description of the foundations and tactics of the cutting-edge algorithms * Real-world case stories open numerous chapters * greater than 500 full-color figures and tables * Edited by way of most sensible distant sensing specialists with contributions from authors around the geosciences.
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Additional info for Advanced Remote Sensing : Terrestrial Information Extraction and Applications
CONCLUDING REMARKS Signiﬁcant progress in engineering issues has enabled remarkable improvements in platform and sensor systems that can be seen in many indicators, such as signal-to-noise ratios, resolutions, pointing accuracies, geometric and spectroradiometric integrity, and calibration. The immense amounts of data available from satellite observations present great challenges. Considerable investments have been put into developing physical models to understand surface radiation regimes, and some of these models have been incorporated into useful algorithms for estimating land surface variables from satellite observations; however, development of realistic and computationally simpliﬁed surface radiation models mostly suitable for inversion of land surface variables from satellite data is still urgently required.
Liang, S. (2004). Quantitative Remote Sensing of Land Surfaces. New York: John Wiley & Sons. Liang, S. ). (2008). Advances in Land Remote Sensing: System, Modeling, Inversion and Application. New York: Springer. Liang, S. L. (2007). Recent developments in estimating land surface biogeophysical variables from optical REFERENCES remote sensing. Progress in Physical Geography, 31, 501e516. , & Weng, Q. (2007). A survey of image classiﬁcation methods and techniques for improving classiﬁcation performance.
Sensor Modeling Since common detector materials do not respond across the entire optical spectrum, most sensors have separate focal planes and noise mechanisms for each spectral region. The sensor model can describe the effects of an imaging spectrometer on the spectral radiance mean and covariance statistics of a land surface. The input radiance statistics of every spectral channel are modiﬁed by electronic gain, radiometric noise sources, and relative calibration error to produce radiance signal statistics that represent the scene as imaged by the sensor.