Remote Sensing Questions Chapter 15

  1. How are remote sensing images obtained or converted into digital form?
  2. Why are digital forms used?
  3. What are the four major areas of computer operations in digital image processing?
  4. What is image restoration or preprocessing?
  5. What is image enhancement?
  6. What is image classification?
  7. What is data-set merging?
  8. Describe the characteristics of a digital image.
  9. How are digital numbers used in application to remote sensing images?
  10. How are these stored and used by computers?
  11. What is the numerical range of values most often associated with remote sensing data?
  12. What color usually represents the DN value of 0?
  13. What color usually the value 255 in an 8-bit scale?
  14. Before digital image data are subjected to computer enhancement and classification routines, what knowledge about the data is usually needed and how is it obtained?
  15. Differentiate between interactive and batch processing and describe the advantages and disadvantages of each for remote sensing work.
  16. What are the parts of a typical minicomputer-based image processing system and what function does each part perform?
  17. What is the main advantage of disk storage over magnetic tape storage?
  18. What is the size in pixels of a MSS Landsat image, a TM Landsat image, a HRV multispectral SPOT image and a HRV panchromatic SPOT image?
  19. How do microcomputer-based image processing systems differ from minicomputer-based image processing systems?
  20. Why does preprocessing precede other steps, and what are four of the computer algorithms that might be used?
  21. Differentiate between systematic and nonsystematic geometric distortions and describe how the different kinds can be minimized or eliminated.
  22. What are the advantages and disadvantages of the use of nearest-neighbor resampling, bilinear interpolation, and cubic convolution?
  23. What is noise, what problems does it create, and how can these be rectified or minimized?
  24. What methods have been used to reduce the effect of atmospheric scattering?
  25. What is the goal of image enhancement?
  26. What are the seven categories of image enhancement?
  27. What is the purpose of contrast stretching.
  28. What are the advantages and disadvantages of linear stretches, area-specific stretches, sinusoidal stretches, and nonlinear stretches?
  29. What is the purpose of filtering?
  30. How does it differ from enhancement?
  31. What are its advantages?
  32. What is edge enhancement and how is it accomplished?
  33. What are the advantages?
  34. How many ratio combinations are possible with the four bands of the MSS?
  35. How many are possible with the TM's six nonthermal bands?
  36. What two statistical processes are used to minimize spectral redundancy while reducing or compressing the dimensionality of the data?
  37. What is the main object of canonical analysis or multiple-discriminant analysis?
  38. Why is it often used as a preprocessing step to image classification?
  39. Differentiate between visual interpretation and image classification.
  40. What are the five basic steps for image classification?
  41. Differentiate between unsupervised classification and supervised classification?
  42. What are advantages to be gained by multisensor image merging?
  43. What are other image variations which computers can produce from the input data?
  44. For ratioing multiband images, what would be the correct order of the following, ratioing, haze removal, contrast stretch?
  45. Describe the X, Y, and Z parameters of a digital image.
  46. Matching--Make the correct pairings.


principal component analysis, spatial filtering, atmospheric haze, CCT, multidate image mosaics, pseudocolor, Gaussian contrast stretch, unsupervised classification, cubic convolution, contrast stretching, bit errors, supervised classification, six-line banding, MTF correction, first differencing

A. image storage

B. point operation

C. arbitrary color assignments

D. sun-angle correction

E. training areas

F. nonuniform expansion

G. periodic noise

H. three directions

I. additive DN components

J. clusters

K. resampling

L. random noise

M. edge enhancement

N. linear data transformation

O. neighborhood operation