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Books on the topic 'Remote sensing - Mathematics'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Mathematical principles of remote sensing. Chelsea, Mich: Sleeping Bear Press, 1999.

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2

1964-, Lubin Dan, ed. Polar remote sensing. Berlin: Springer, in association with Praxis Publishing, 2004.

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3

Weng, Qihao. Scale issues in remote sensing. Hoboken, N.J: John Wiley & Sons, 2014.

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4

Cracknell, Arthur P. Introduction to remote sensing. 2nd ed. Boca Raton, FL: CRC Press, 2005.

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5

Ladson, Hayes, ed. Introduction to remote sensing. 2nd ed. Boca Raton, FL: CRC Press, 2007.

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6

Ladson, Hayes, ed. Introduction to remote sensing. London: Taylor & Francis, 1991.

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7

1958-, Graña Manuel, and Duro Richard J. 1965-, eds. Computational intelligence for remote sensing. Berlin: Springer, 2008.

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8

Lee, Chulhee. Feature extraction and classification algorithms for high dimensional data. West Lafayette, Ind: School of Electrical Engineering, Purdue University, 1993.

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9

K, Ghosh S., ed. Remote sensing and geographical information system. Oxford: Alpha Science, 2006.

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10

Lillesand, Thomas M. Remote sensing and image interpretation. 3rd ed. New York: Wiley, 1994.

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11

Lillesand, Thomas M. Remote sensing and image interpretation. 6th ed. Hoboken, NJ: John Wiley & Sons, 2007.

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12

Lillesand, Thomas M. Remote sensing and image interpretation. 2nd ed. New York: Wiley, 1987.

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13

W, Kiefer Ralph, ed. Remote sensing and image interpretation. 2nd ed. New York: Wiley, 1987.

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14

Lillesand, Thomas M. Remote sensing and image interpretation. 5th ed. New York, NY: John Wiley & Sons, 2002.

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15

W, Kiefer Ralph, and Chipman Jonathan W, eds. Remote sensing and image interpretation. 5th ed. New York: Wiley, 2004.

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16

W, Kiefer Ralph, ed. Remote sensing and image interpretation. 3rd ed. New York: Wiley & Sons, 1994.

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17

W, Kiefer Ralph, ed. Remote sensing and image interpretation. 4th ed. New York: John Wiley & Sons, 2000.

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18

Yao gan shu ju zhi neng chu li fang fa yu cheng xu she ji. 2nd ed. Bei jing: Ke xue chu ban she, 2010.

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19

R, Brooks S., and Institute of Mathematics and Its Applications., eds. Mathematics in remote sensing: Based on the proceedings of a Conference organized by the Institute of Mathematics and its Applications on Mathematics and Its Application in Remote Sensing held at Danbury, Essex in May 1986. Oxford: Clarendon Press, 1989.

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20

Borengasser, Marcus. Hyperspectral remote sensing: Principles and applications. Boca Raton: CRC Press, 2008.

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21

Marcolongo, Bruno. Photogeology: Remote sensing applications in earth science. Enfield, NH: Science Publishers, Inc., 1997.

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22

Twomey, S. Introduction to the mathematics of inversion in remote sensing and indirect measurements. Mineola, N.Y: Dover Publications, 1996.

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23

Kim, Hakil. A method of classification for multisource data in remote sensing based on interval-valued probabilities. West Lafayette, Ind: Laboratory for Applications of Remote Sensing, Purdue University, 1990.

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24

Dermanis, Athanasios. Telerilevamento: Informazione territoriale mediante immagini da satellite. Milano: Ambrosiana, 2002.

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25

Czaplewski, Raymond L. Variance approximations for assessments of classification accuracy. Fort Collins, Colo. (240 W. Prospect Rd., Fort Collins 80526): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1994.

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26

Day, Charles. A compilation of PEDITOR estimation formulas. Washington, DC: U.S. Dept. of Agriculture, National Agricultural Statistics Service, Research and Development Division, 2002.

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27

Kass, Green, ed. Assessing the accuracy of remotely sensed data: Principles and practices. 2nd ed. Boca Raton: CRC Press/Taylor & Francis, 2008.

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28

Congalton, Russell G. Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton, Fla: Lewis, 1999.

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29

Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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30

Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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31

Susaki, Jun'ichi. Kūkan jōhōgaku. Tōkyō-to Bunkyō-ku: Koronasha, 2013.

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32

Pierson, Willard J. Final report on remote sensing research for NASA grant NAGW-690. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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33

Mathematicsin remote sensing. Oxford: Clarendon, 1989.

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34

Remote Sensing of Turbulence. Taylor & Francis Group, 2021.

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35

Wavelets for Sensing Technologies (Artech House Remote Sensing Library). Artech House Publishers, 2003.

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36

Weng, Qihao. Scale Issues in Remote Sensing. Wiley & Sons, Limited, John, 2014.

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37

Weng, Qihao. Scale Issues in Remote Sensing. Wiley & Sons, Incorporated, John, 2014.

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38

Weng, Qihao. Scale Issues in Remote Sensing. Wiley & Sons, Incorporated, John, 2014.

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39

Weng, Qihao. Scale Issues in Remote Sensing. Wiley & Sons, Incorporated, John, 2014.

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40

Hyperspectral Remote Sensing. CRC, 2004.

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41

Hungate, William S., Russell Watkins, and Borengasser Marcus Hungate William S. Hyperspectral Remote Sensing. Taylor & Francis Group, 2007.

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42

Cracknell, Arthur P. Introduction to Remote Sensing. Taylor & Francis Group, 2007.

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43

Cracknell, Arthur P. Introduction to Remote Sensing. Taylor & Francis Group, 2007.

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44

Hayes, Ladson, A. P. Cracknell, and Arthur P. Cracknell. Introduction to Remote Sensing. Taylor & Francis Group, 2007.

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45

Loots, Linda, Claris de Lange, and Klarin Loots. Study and Master Mathematics Grade 9. Roedurico Trust, 1996.

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46

Loots, Karin, Linda Loots, and Claris de Lange. Study and Master Mathematics Grade 8. Roedurico Trust, 1997.

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47

Ham, J., E. A. Bester, K. Loots, and A. Stark. Study and Master Mathematics Grade 11 and 12 SG. Roedurico Trust, 1998.

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48

Twomey, S. Introduction to the Mathematics of Inversion in Remote Sensing. Dover Publications, 2002.

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49

Cracknell, Arthur P., and Ladson Hayes. Introduction to Remote Sensing, Second Edition. 2nd ed. CRC, 2007.

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50

Milman, Andrew S. Mathematical Principles of Remote Sensing: Making Inferences from Noisy Data. Taylor & Francis Group, 2000.

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