Remote Sensing Courses
FOR 4214: Forest Photogrammetry & Spatial Data Processing
FOR 5254: Remote Sensing of Natural Resources
FOR/GEOG 5104: Interdisciplinary Seminar in Remote Sensing and GIS
GEOG 4354: Introduction to Remote Sensing
CS 5814: Digital Picture Processing
ECE 5524: Pattern RecognitionCS 5554: Computer Vision Systems
CS 5034: Analysis of Spatial Data
GEOG 5124: Aerial Photointerpretation and Analysis
FOR/GEOG 6984: Forestry Lidar Applications
NR 6104: Advanced Topics in Remote Sensing
FOR/GEOG 5984: Hyperspectral Remote Sensing for Natural Resources
Philosophy and rationale of remote sensing as a part of the resource management process; comparisons of analogic and digital sensors; sensor selection and proper use; accuracy assessment; signature development; and identification of factors which affect the quality of remotely sensed information. Pre: 4214 or GEOG 4354. (2H,3L,3C)
Interdisciplinary seminar devoted to current research in the fields of remote sensing, Geographic Information Systems, and related topics. Seminars, workshops, and presentations conducted by students, faculty, and visitors. Pre: Graduate standing. Pass/Fail only. (1H,1C).
Films, filters and camera photogeometry; scale; measurement estimation; image processing; flight planning and photo acquisition; geographic information systems; spatial data analysis techniques and applications. Senior standing required. (2H,3L,3C) I.
Theory and methods of remote sensing. Practical exercises in interpretation of aerial photography, satellite, radar, and thermal infrared imagery. Digital analysis, image classification, and evaluation. Applications in earth sciences, hydrology, plant sciences, and land use studies. (2H,3L,3C).
Representation and processing of greytone images. Construction and simulation of grey scales, digitization, thresholding, local neighborhood operations, template matching and filtering, enhancement and restoration, segmentation, connected components, matching, morphology. Pre: 1704, MATH 1114. (3H,3C).
Statistical pattern recognition: review of statistical basis for pattern recognition; Bayes theorem; estimation and learning; non-parametric techniques; feature extraction; linear and piecewise-linear discriminant functions; clustering; hierarchical recognition systems. Syntactic pattern recognition: review of automata and language theory; shape descriptors; syntactic recognition systems; grammatical inference and learning. Additional topics: fuzzy set theory; hybrid pattern recognition. Pre: 5505, STAT 4714. (3H,3C).
Gives a critical examination of current theories of computer vision. Explores both image analysis and scene analysis methods with the emphasis being given to scene analysis techniques. Emphasis is placed on the strategies that can be used rather than upon particular operators. Gives the design trade-offs associated with the various strategies. Draws analogies between computer vision techniques and the operations that are seemingly performed in human vision. Co: CS 5534, CS 5814. (3H,3C).
Methods of describing and analyzing spatial distributions, including spatial autocorrelation, quadrant analysis, trend surface analysis, and methods of map comparison. Applications to student research problems. Pre: STAT 4102.
Principles, history, and methods of aerial photographic interpretation. Introduction to photographic systems and application to aerial photography. Human dimension to photointerpretation. Applications to varied fields of knowledge such as land-use mapping, earth sciences, forestry, agriculture, history and archaeology, and military and strategic studies. Pre: 4354, FOR 4214, or equivalent. (3H, 3C)
In-depth coverage of advanced topics in the field of remote sensing selected to cover emerging techniques and technologies. Topics included are those too specialized to form components of the regular curriculum. Examples of topics, which will differ each semester, include field data in support of remote sensing, accuracy assessment, and hyperspectral remote sensing. Pre: GEOG/GEOS 4354, For 5254, and GEOG/FOR 5104 (2H, 3L, 3C)
Theoretical underpinning of established and emerging research using light detection and ranging (lidar) technology for forestry applications including detailed terrain mapping and digital elevation models, canopy height modeling, prediction of forest biophysical parameters, forest physiology and the canopy light regime, watershed mapping and stream modeling, ecological modeling, landscape classifications, and wildlife habitat. Advanced research tools and techniques used to analyze lidar data for different applications. Pre: Advanced graduate standing, (2H, 3L, 3C) I.
Theory of spectroscopy and spectrometry from portable spectroradiometers to airborne and spaceborne hyperspectral sensors as relevant to natural resource applications; atmospheric correction to surface reflectance; hyperspectral image analysis techniques; hyperspectral vegetation indices; application of hyperspectral remote sensing for analysis of canopy biophysical parameters, vegetation species identification (in forest and urban environments), foliar biochemistry and vegetative health (at leaf and canopy scales), soil and peat properties, mineral and geothermal characteristics, water applications, and other natural resources applications. Practical ‘hands-on’ investigation of the research tools and techniques used to analyze hyperspectral data for various natural resources applications, (2H, 3L, 3C) I.