GIS4035 Photo Interpretation and Remote Sensing, Dr. Brian Fulfrost
Supervised Image Classification
Lab description - Students utilized unsupervised classification methods to derive Land Use Land Cover off of a satellite image. This week,the students were assigned to utilize supervised classifications to derive Land Use Land Cover (LULC) classification off of the spectral information contained in the Digital Numbers (DN) stored in remotely sensed imagery. Supervised classification differs from unsupervised methods in its uses of "training sites" (based on a priori knowledge often from ground observations of the information classes being mapped) to guide the classification of the image from spectral values into information (e.g. LULC) classes. It also differs from unsupervised classification in its use of statistics (as opposed to Euclidean spectral distance) to assign pixel values to information classes.
Student Learning Outcomes:
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STUDENT SPOTLIGHT AWARDS
The following student was chosen for their exceptional work on the Supervised Image Classification Lab assignment:
Gail Sease
About Gail: Gail lives in Bakersfield, CA has earned BS and MS degrees in geology but has not worked as a geologist for a long time. Her occupations over the last 20 years have included oil company geologist, junior college geology instructor, Spanish student, teacher of middle school and high school Spanish, biology and geology, school librarian and school secretary. Before moving to Bakersfield in 2011, she lived with her family in Bogot
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Supervised Image Classification
Lab description - Students utilized unsupervised classification methods to derive Land Use Land Cover off of a satellite image. This week,the students were assigned to utilize supervised classifications to derive Land Use Land Cover (LULC) classification off of the spectral information contained in the Digital Numbers (DN) stored in remotely sensed imagery. Supervised classification differs from unsupervised methods in its uses of "training sites" (based on a priori knowledge often from ground observations of the information classes being mapped) to guide the classification of the image from spectral values into information (e.g. LULC) classes. It also differs from unsupervised classification in its use of statistics (as opposed to Euclidean spectral distance) to assign pixel values to information classes.
Student Learning Outcomes:
<div>
- Create spectral signatures and AOI feature
- Produce classified images from satellite data
- Recognize and eliminate spectral confusion between spectral signatures
STUDENT SPOTLIGHT AWARDS
The following student was chosen for their exceptional work on the Supervised Image Classification Lab assignment:
Gail Sease
أكثر...