Remote Sensing Exam
Multiple Choice Questions (2*20 points)
1. What are the objectives of information extraction from remotely sensed data?
A. Identification of similar pixels.
B. Grouping of thematic information.
C. Interpretation using prior knowledge.
D. Making sense of geographic patterns.
E. All of the above.
2. Principal components analysis is associated with which of the following?
A. Data reduction.
B. Training samples.
C. Components and Eigenvectors.
D. A and C.
E. A, B and C.
3. What are spectral classes?
A. Groups of pixels with similar spectral values.
B. Groups of pixels with similar thematic information.
C. Groups of pixels with similar reflectance properties.
D. A and B.
E. A and C.
4. What are information classes
A. Groups of pixels with similar spectral values.
B. Groups of pixels with similar thematic information.
C. Groups of pixels with similar reflectance properties.
D. A and B.
E. A and C.
5. What should training samples ideally represent?
A. Unimodal distribution of pixels.
B. Bimodal distribution of pixels.
C. As much spectral variation within one class as possible.
D. A and C.
E. B and C.
6. Which resolution is derived from the IFOV at both nadir and off-nadir?
A. Spectral.
B. Radiometric.
C. Spatial.
D. Temporal.
E. A and B.
7. What is the sequence of bands 3 through 6 of the Landsat TM-5 sensor?
A. NIR, NIR, Red, Green.
B. Green, Red, Near Infrared, NIR.
C. Red, NIR, NIR, Thermal Infrared.
D. Blue, Green, Red, NIR.
E. Red, NIR, Thermal Infrared, Mid Infrared.
8. Which of the following aggregate surfaces would have the HIGHEST reflective qualities (all things being equal) in the NEAR INFRARED parts of the spectrum?
A. Trees and grass.
B. Water and bare soil.
C. Bare soil and urban.
D. Water and crops.
E. Trees and bare soil.
9. Which of the following aggregate surfaces would have the LOWEST reflective qualities (all things being equal) in the VISIBLE parts of the spectrum?
A. Trees and
1. What are the objectives of information extraction from remotely sensed data?
A. Identification of similar pixels.
B. Grouping of thematic information.
C. Interpretation using prior knowledge.
D. Making sense of geographic patterns.
E. All of the above.
2. Principal components analysis is associated with which of the following?
A. Data reduction.
B. Training samples.
C. Components and Eigenvectors.
D. A and C.
E. A, B and C.
3. What are spectral classes?
A. Groups of pixels with similar spectral values.
B. Groups of pixels with similar thematic information.
C. Groups of pixels with similar reflectance properties.
D. A and B.
E. A and C.
4. What are information classes
A. Groups of pixels with similar spectral values.
B. Groups of pixels with similar thematic information.
C. Groups of pixels with similar reflectance properties.
D. A and B.
E. A and C.
5. What should training samples ideally represent?
A. Unimodal distribution of pixels.
B. Bimodal distribution of pixels.
C. As much spectral variation within one class as possible.
D. A and C.
E. B and C.
6. Which resolution is derived from the IFOV at both nadir and off-nadir?
A. Spectral.
B. Radiometric.
C. Spatial.
D. Temporal.
E. A and B.
7. What is the sequence of bands 3 through 6 of the Landsat TM-5 sensor?
A. NIR, NIR, Red, Green.
B. Green, Red, Near Infrared, NIR.
C. Red, NIR, NIR, Thermal Infrared.
D. Blue, Green, Red, NIR.
E. Red, NIR, Thermal Infrared, Mid Infrared.
8. Which of the following aggregate surfaces would have the HIGHEST reflective qualities (all things being equal) in the NEAR INFRARED parts of the spectrum?
A. Trees and grass.
B. Water and bare soil.
C. Bare soil and urban.
D. Water and crops.
E. Trees and bare soil.
9. Which of the following aggregate surfaces would have the LOWEST reflective qualities (all things being equal) in the VISIBLE parts of the spectrum?
A. Trees and