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Advanced Remote Sensing

by: Ferne Wiza

Advanced Remote Sensing GEOG 577

Ferne Wiza
GPA 3.64

Conghe Song

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Conghe Song
Study Guide
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This 2 page Study Guide was uploaded by Ferne Wiza on Sunday October 25, 2015. The Study Guide belongs to GEOG 577 at University of North Carolina - Chapel Hill taught by Conghe Song in Fall. Since its upload, it has received 38 views. For similar materials see /class/228661/geog-577-university-of-north-carolina-chapel-hill in Geography at University of North Carolina - Chapel Hill.

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Date Created: 10/25/15
Study Guide Advanced Remote Sensing Spring 2008 Concepts Change detection Multidate composite image Guided Clustering End member Spectral library Maximum likelihood classi cation Parallepiped classi cation Kmeans clustering Khat statistic 0 spectral space HPW QP39HeWNEEquot 11 Questions 1 What does Principal Component Analysis do to a multispectral image 2 What does Tasseled Cap Transformation do to a multispectral image 3 How does PCA and TC Transformation differ in transforming an image 4 Given the Eigen values for a PCA of a multispectral image M M 7t how much information does each of the component contain from the original image 5 Given Eigen vector associated with the first Eigen vector 1 N for a Landsat TM image and DN values for the 6 bands for a pixel are a S S S S S u a Dd1 d2 d3 d4 d5 d5 what is the PC value for the pixel 6 If we have a multispectral image A we denote the image as Ac after correcting for atmospheric effects and A0 as the raw DN image We perform a PCA on AC and A0 The resulting PCA images are PCAc and PCAO respectively Will the first component of PCAc contain the same amount of information from Ac as the first component of PCA0 from A0 Explain why 7 explain the following change detection approach 1 image differencing 2 image ratioing 3 change vector analysis 4 postclassi cation comparison 8 What are the key assumptions of spectral mixture analysis 9 What is Ridd s VIS model for spectral mixture analysis What might be the potential problem with it 10 What are the key steps in spectral mixture analysis 11 What are the key differences in maximum likelihood classi cation and arti cial neural network classi cation 12 How do the spectral clusters in unsupervised classi cation differ from the classes in landcoverlanduse classi cation


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