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  • Other Sources  (3)
  • Earth Resources and Remote Sensing  (2)
  • EARTH RESOURCES AND REMOTE SENSING  (1)
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  • 1
    Publication Date: 2013-08-31
    Description: Multiple images taken from similar locations and under similar lighting conditions contain similar, but not identical, information. Slight differences in instrument orientation and position produces mismatches between the projected pixel grids. These mismatches ensure that any point on the ground is sampled differently in each image. If all the images can be registered with respect to each other to a small fraction of a pixel accuracy, then the information from the multiple images can be combined to increase linear resolution by roughly the square root of the number of images. In addition, the gray-scale resolution of the composite image is also improved. We describe methods for multiple image registration and combination, and discuss some of the problems encountered in developing and extending them. We display test results with 8:1 resolution enhancement, and Viking Orbiter imagery with 2:1 and 4:1 enhancements.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Lunar and Planetary Inst., The Twenty-Fifth Lunar and Planetary Science Conference. Part 1: A-G; p 241-242
    Format: application/pdf
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  • 2
    Publication Date: 2020-01-07
    Description: No abstract available
    Keywords: Earth Resources and Remote Sensing
    Type: JPL-CL-16-2915 , Workshop on Remote sensing in the O2 A-band; Jul 08, 2016; De Bilt; Netherlands
    Format: text
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  • 3
    Publication Date: 2019-07-10
    Description: This research note shows the results of applying a new massively parallel version of the automatic classification program (AutoClass IV) to a particular Landsat/TM image. The previous results for this image were produced using a "subsampling" technique because of the image size. The new massively parallel version of AutoClass allows the complete image to be classified without "subsampling", thus yielding improved results. The area in question is the FIFE study area in Kansas, and the classes AutoClass found show many interesting subtle variations in types of ground cover. Displays of the spatial distributions of these classes make up the bulk of this report. While the spatial distribution of some of these classes make their interpretation easy, most of the classes require detailed knowledge of the area for their full interpretation. We hope that some who receive this document can help us in understanding these classes. One of the motivations of this exercise was to test the new version of AutoClass (IV) that allows for correlation among the variables within a class. The scatter plots associated with the classes show that this correlation information is important in separating the classes. The fact that the spatial distribution of each of these classes is far from uniform, even though AutoClass was not given information about positions of pixels, shows that the classes are due to real differences in the image.
    Keywords: Earth Resources and Remote Sensing
    Type: FIA-94-01
    Format: text
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