DIGITAL IMAGE PROCESSING IAS-552 by umf86597

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									    FALL TERM
DIGITAL IMAGE PROCESSING
IAS-552 (2 credit hours)
This Course familiarizes students with the state-of-the-art digital image processing techniques and
practice applications of remote sensing in various fields. Course objective is to demonstrate present
applications of remote sensing with software training in ENVI+IDL, SARscape, and provide students
with the skills and knowledge to apply remote sensing to their own research problems.

Cross-registration is recommended for courses IAS-552 and IAS-551

STUDENTS IN THIS COURSE:                               TOPICS COVERED:
   Engage in hands-on lab training with               -   images, arrays & matrices
    ENVI+IDL, SARscape                                 -   Image preprocessing: conversion between
   Learn state-of-the-art image processing                digital numbers, radiance, at-sensor and
    techniques                                             surface reflectances
   Discuss and review recent advances and             -   Correction for atmospheric and topographic
    current trends in the subject area                     effects
   Practice the applications of the techniques        -   3D scene generation using optical, RADAR
    through hands-on projects geared toward                and LiDAR data, and vegetation analysis
    environmental studies, resources mapping,          -   Image Classification techniques
    disaster monitoring and mitigation,                -   Image transform & enhancement and data
    inventorying plant species, minerals                   fusion: band ratios, PCA, PAN sharpening
    abundance, and mapping sub-marine                  -   Hyperspectral image processing techniques
    springs, etc.                                      -   Feature extraction: extracting frogs, fish,
                                                           roads and U.S. dollars from satellite images

Synthetic Aperture Radar
Applications



    Forest Health            Tree classification             Feature extraction         3D scene generation




DATES:                     FACULTY:                REGISTRATION: 314-977-2269
M:                         Abuduwasiti Wulamu      https://ssbprd.slu.edu/ssbprd/bwckschd.p_disp_dyn_sched
3:10AM-5:00PM              ghulam@eas.slu.edu
                                                   For more information visit WWW.CES.SLU.EDU/crs/
                           314-977-7062

								
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