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Title: :  Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier
PaperId: :  10055
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 5 Issue 2 2019
DUI:    16.0415/IJARIIE-10055
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Shivraj SAlvas institute of engineering and technology
Suresh M NaragundAlvas institute of engineering and technology
Yeshwanth MAlvas institute of engineering and technology
Prakash NaikAlvas institute of engineering and technology

Abstract

Electronics and Communication Engineering
Remote sensing, image classification, decision tree classifier (DTC), maximum likelihood classifier (MLC).
Remote sensing is the science and art of obtaining information about an object through the analysis of data acquired by a device that is not in contact with the object. Remotely sensed data can be of many forms, including variations in force distribution, acoustic wave distribution or electromagnetic energy distributions and can be obtained from a variety of platforms, including satellite, airplanes, remotely pilot vehicles, handheld radiometers or even bucket trucks. They may be gathered by different devices, including sensors, film camera, digital cameras, video recorders. Our eyes acquire data on variations in electromagnetic radiations. Instruments capable of measuring electromagnetic radiation are called sensors. Sensors can be differentiated in two main groups: Passive sensors: without their own source of radiation. They are sensitive only to radiation from a natural origin. Active sensors: which have a built in source of radiation. Examples are Radar and Lidar systems.In this project, an attempt has been made to develop a decision tree classification algorithm for remotely sensed satellite data using the separability matrix of the spectral distributions of probable classes in respective bands. The spectral distance between any two classes is calculated from the difference between the minimum spectral value of a class and maximum spectral value of its preceding class for a particular band. The decision tree is then constructed by recursively partitioning the spectral distribution in a Top-Down manner. Using the separability matrix, a threshold and a band will be chosen in order to partition the training set in an optimal manner. The classified image is compared with the image classified by using classical method Maximum Likelihood Classifier (MLC).

Citations

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IJARIIE Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. "Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier" International Journal Of Advance Research And Innovative Ideas In Education Volume 5 Issue 2 2019 Page 2503-2506
MLA Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. "Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier." International Journal Of Advance Research And Innovative Ideas In Education 5.2(2019) : 2503-2506.
APA Shivraj S, Suresh M Naragund, Yeshwanth M, & Prakash Naik. (2019). Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier. International Journal Of Advance Research And Innovative Ideas In Education, 5(2), 2503-2506.
Chicago Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. "Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier." International Journal Of Advance Research And Innovative Ideas In Education 5, no. 2 (2019) : 2503-2506.
Oxford Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. 'Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier', International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 2, 2019, p. 2503-2506. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Pixel_Based_Classification_of_Multi_spectral_Remote_Sensed_Data_using_Decision_Tree_Classifier_ijariie10055.pdf (Accessed : ).
Harvard Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. (2019) 'Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier', International Journal Of Advance Research And Innovative Ideas In Education, 5(2), pp. 2503-2506IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Pixel_Based_Classification_of_Multi_spectral_Remote_Sensed_Data_using_Decision_Tree_Classifier_ijariie10055.pdf (Accessed : )
IEEE Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik, "Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier," International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 2, pp. 2503-2506, Mar-App 2019. [Online]. Available: https://ijariie.com/AdminUploadPdf/Pixel_Based_Classification_of_Multi_spectral_Remote_Sensed_Data_using_Decision_Tree_Classifier_ijariie10055.pdf [Accessed : ].
Turabian Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. "Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 5 number 2 ().
Vancouver Shivraj S, Suresh M Naragund, Yeshwanth M, and Prakash Naik. Pixel Based Classification of Multi-spectral Remote Sensed Data using Decision Tree Classifier. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2019 [Cited : ]; 5(2) : 2503-2506. Available from: https://ijariie.com/AdminUploadPdf/Pixel_Based_Classification_of_Multi_spectral_Remote_Sensed_Data_using_Decision_Tree_Classifier_ijariie10055.pdf
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