Differentiate supervised classification from unsupervised classification
Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
Compare pixel-based image classification methods with segmentation techniques
Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
Describe an application of hyperspectral image data
Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
Describe a situation in which filtered data are more useful than the original unfiltered data
Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
Differentiate supervised classification from unsupervised classification
Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
Compare pixel-based image classification methods with segmentation techniques
Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
Describe an application of hyperspectral image data
Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
Describe a situation in which filtered data are more useful than the original unfiltered data
Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
Differentiate supervised classification from unsupervised classification
Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
Compare pixel-based image classification methods with segmentation techniques
Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
Describe an application of hyperspectral image data
Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
Describe a situation in which filtered data are more useful than the original unfiltered data
Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity
Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earth’s atmosphere is sufficiently transparent to allow high-altitude remote sensing
Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)
Describe an application that requires integration of remotely sensed data with GIS and/or GPS data
Explain the concept of “data fusion” in relation to remote sensing applications in GIS&T
Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum
Differentiate supervised classification from unsupervised classification
Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
Compare pixel-based image classification methods with segmentation techniques
Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
Describe an application of hyperspectral image data
Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
Describe a situation in which filtered data are more useful than the original unfiltered data
Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
DC-19 - Ground verification and accuracy assessment