Remote Sensing Chapter 15 Vocabulary

"master-slave" technique, 16-line striping pattern, area-specific stretch, arithmetic mean, artificial data, artificial parallax, automated interpretation, Band Sequential format, batch processing, bilinear interpolation, bit errors, bits, box, CCTs, central processing unit, clean-up, clusters, components, Computer Software Management and Information Center (COSMIC), computer-compatible tapes, contrast stretching, cubic convolution, curvature distortion, data base, data classification, deskewed, destriping, digital numbers, digital image processing, digital merging, digital elevation models, digital image, digital form, directional filters, directional first differencing, documentation, edge enhancement, edges, electronic noise, evaluation and refinement, film digitizer, film writer, Gaussian stretch, generation of statistical parameters, geometric corrections, ground control points, ground resolution cells, hardwired, haze correction technique, high-frequency component, high-pass spatial filtering, high-pass filter, histogram trimming, histograms, hybrid configuration, image enhancement, image processing services, image classification, image restoration, image display subsystem, image processing systems, interactive prompts, interactive processing, interactive menu, line drops, linear stretch, linear data transformations, lines, low-frequency component, low-pass filter, low-pass spatial filtering, magnetic tape, median, modular construction, multidimensional image classification, multisensor image merging, multispectral band ratioing, multitemporal processing, nearest-neighbor resampling, noise correction, nonlinear stretches, nonsystematic geometric distortions, nonuniform expansion, normal distribution, Optimum Index Factor, overview, pels, periodic noise, pixels, plotter, pseudocolor, radiometric corrections, RAM, random distortions, random noise, registration, resampling, rescaling, samples, simulated natural color, sinusoidal stretch, six-line banding, skew coefficient, skew, spatial filtering, spectral signature, square filter, standard deviation, subsetting, sun-angle correction, supervised classification, surface interpolation, synergism, systematic geometric distortions, temporal-difference image, temporal-ratio image, training class selection, uniform distribution stretch, uniform expansion, unsupervised classification

  1. Coded values for radiation measurements
  2. Visual representation of the numerical values measured by remote sensing instruments
  3. Manipulation of numerical values from remote sensing instruments in the form of preprocessing, image enhancement, image classification, and data-set merging
  4. Individual resolution units, picture elements
  5. Contraction of the more common term for picture elements
  6. Small terrain areas represented by one measurement in remote sensing
  7. Horizontal row of pixels
  8. Vertical column of pixels
  9. Positive integers indicating the amount of radiation measured by remote sensing instruments, DNs
  10. Binary digits, 0 or 1, off or on
  11. Expansion of 6- or 7- bit data to the 0-255 scale
  12. Column diagrams used to display DN frequency distributions
  13. Measure of central tendency found by summing the values and dividing by the number of observations
  14. Measure of central tendency represented by the middle value
  15. Measure of dispersion which is the average amount of variation
  16. Measure of the departure from symmetry
  17. Bell-shaped curve, Gaussian distribution
  18. Operator-analyst directly controls the computer running the program
  19. Job submitted to the computer as a full set of instructions performed by the computer without further operator input
  20. Combination of interactive and batch processing, complementary configuration
  21. Hardware and software designed to manipulate digital remote sensing data
  22. Built-in expansion and modification capability using a building-block concept to design the architecture of the computer software, like object oriented programming
  23. A federally funded facility that sells computer programs developed under government sponsorship
  24. Functions directory, lists of command modules that execute a task within a computer program
  25. Windows or menus that direct a user through a task
  26. Programs burned or built permanently into the computer's circuitry
  27. Performs arithmetic and control functions within the computer, CPU
  28. Direct-access device for storage of image data and software on mainframe systems, particularly used in the past for LANDSAT images
  29. Consists of microprocessor, refresh memory, monitor, and a cursor-control device
  30. Random-access memory
  31. Processing technique that arbitrarily assigns color to image brightness
  32. Data reduction process where every nth line and sample
  33. A sampling technique that reduces resolution but permits visual selection of subareas for detailed analysis at full or improved pixel resolution
  34. Distortion resulting from the format of monitors, barrel distortion
  35. Produces photographic film of digital data
  36. Output device that uses pen and ink to produce maps
  37. Scans color or monochrome films and converts film density variations into digital numbers for computer processing
  38. Adjustments that take into account calibration factors for the detectors
  39. Adjustments that take into account earth rotation and variation in viewing angle
  40. Data stored sequentially in a separate image file for all scan lines in the image array, BSQ
  41. Preprocessing
  42. Image correction, the first step in image restoration
  43. Image that has been preprocessed
  44. Predictable variations to the image that are constant over time
  45. Result from altitude and attitude variations and topographic elevation differences
  46. A systematic distortion that results from the earth's rotation and the satellite's orbital movements
  47. Results when an algorithm shifts scan lines by a calculated number of pixels
  48. Nonsystematic distortions
  49. Identifiable locations on the distorted image and a reference map or control base
  50. The interpolation of new pixel values
  51. DN equal to the nearest input pixel is assigned to the output pixel
  52. Output DNs are determined by taking a proximity-weighted average of input DNs from the four nearest pixels
  53. Output DNs are assigned on the basis of a weighted average of input DNs from 16 surrounding pixels
  54. Input that is independent of the information transmitted from the scene, extraneous input
  55. Extraneous input that is irregular, non-valid image data
  56. Extraneous input that masks radiance data with valid and invalid data such as six-line banding
  57. Periodic noise typically associated with uncorrected Landsat MSS images, a striping effect
  58. Removes or suppresses the periodic noise patterns
  59. A bit error involving speckle or spikes noise where a pixel value differs significantly from those of the surrounding pixels
  60. A bit error where data in horizontal lines is anomalous
  61. Data input to remove errors not based on real measured values
  62. Correction of data values by dividing the DNs for each pixel by the cosine of the illumination angle to normalize the solar illumination
  63. Subtracting a pixel value from all values in that band as a first-order approximation of the effect of atmospheric scattering
  64. The purpose is to improve the detectability of objects or patterns in a digital image for visual interpretation and involves contrast stretching, spatial filtering, edge enhancement, directional first differencing, multispectral band ratioing, simulated natural color, and linear data transformations
  65. Accentuates the contrast between features of interest by redistributing a range of input digital numbers to fill a larger output scale
  66. Enhances different scales of tonal or DN roughness based on the pixel value and those surrounding it
  67. Enhanced rapid changes in DN levels from one pixel to the next to show edges
  68. Approximates the first derivative and designed to highlight the edge information in a digital image by simple DN subtraction
  69. Enhances subtle spectral-reflectance or color difference
  70. Removes atmospheric haze and accentuates subtle color differences by predicting the blue part of the spectrum
  71. Minimizes the spectral redundancy through PCA and CA
  72. Linear contrast stretching
  73. Nonlinear contrast stretching
  74. Increases the contrast in a single digital image while preserving original radiance relationships by assigning new DNs to each pixel with the linear relationship
  75. Increasing the contrast between features by truncating to create a narrower range of input DNs
  76. Enhancement to a portion of an image
  77. Enhances subtle difference within "homogeneous" units, sine stretch
  78. With flexible parameters, these adjustments are controlled by DN pixel frequencies and the shape of the original distribution
  79. Original DN values are redistributed on the basis of their frequency of occurrence with the greatest contrast enhancement occurring within the range with the most original DNs
  80. Forces a skewed frequency distribution of input data to a normal or nonskewed distribution and prevents saturation while enhancing overall scene contrast
  81. Represents gradual DN changes over a relatively large number of pixels
  82. Represents rapid DN changes over a short space
  83. Spatial filters that pass high frequencies and emphasize the details of an image
  84. Spatial filters that produce image smoothing by suppressing the high spatial frequencies
  85. A subarray or window of N and M pixels moved through the larger image array
  86. Filters with uniform weights so that enhancement is equal in all directions
  87. Provide maximum enhancement to features trending perpendicular to the long axis of the filter, rectangular filter
  88. Implemented by calculating a local DN average of an N-by-M digital array or window centered around the pixel being processed
  89. Run on an image to help identify and map structural geologic features including faults, fractures, and monocline, that are characterized by different spatial frequency ranges, because it enhances features that are less than half the size of the window being used while de-emphasizing features that are larger
  90. Abrupt changes that may be a boundary
  91. A noise anomaly associated with the Landsat TM due to slight errors in the internal calibration system or variation in the response of the arrays
  92. Ranks all possible ratio combinations according to the amount of correlation and total variance between the various ratios under consideration
  93. New, transformed variables
  94. Automated digital analysis
  95. Information extraction process, machine interpretation
  96. Defining image properties that represent a group of information, identification of an image area to use to match to other like areas
  97. Statistical algorithms used to define the unique spectral characteristics of a training area
  98. Characteristic pattern of pixel values
  99. Use of the "trained" classification algorithm to assign each pixel to one of the training class categories
  100. Assessment of the classification accuracy and reiteration of the process
  101. Production of maps and tabular data summaries of the classification process
  102. An automatic clustering algorithm that analyses the unknown pixels in the data base and divides them into groups in n-spectral dimensions
  103. Spectrally distinct classes based upon their natural groupings
  104. Classification into groups based upon areas of the known groups and training areas
  105. Co-registration
  106. Surface generation
  107. Matching locations on one image to those on another
  108. Matching one image to another by spatial manipulation
  109. Multidate processing
  110. Subtraction of the image for one date from that of another
  111. Image that has two different dates ratioed so that the first-order brightness variation caused by topography is removed
  112. The merged images yield more information than the sum of the separate images
  113. DEMs
  114. Combination of multispectral and ancillary information into the classification algortihm
  115. Created by a computer algorithm to produce a stereoscopic mate to an image
  116. Company that offers digital processing of remotely sensed data