Explore the Latest Techniques and Trends . in Remotely Sensed Digital Image Analysis!. . . Written in easy-to-follow language with a minimum of technical jargon, Digital Analysis of Remotely Sensed Imagery provides exhaustive coverage of the entire process of analyzing remotely sensed data for the purpose of producing accurate representations in thematic map format. The book explores cutting-edge techniques and trends in image analysis, as well as the relationship between image processing and other recently emerged special technologies.. . Filled with numerous references to the current literature, this essential imaging resource paints a vivid picture of the current status of innovative image analysis methods and future directions in the field. Find state-of-the-art information on storage of remotely sensed datathe image analysis systemimage rectificationimage enhancementimage classification...accuracy assessmentchange detectionintelligent image classificationdecision tree classificationintegration of image analysis with GIS/GPSand much more. Digital Analysis of Remotely Sensed Imagery features:. . Comprehensive, up-to-date coverage of remotely sensed image processing. Details on the relationship between image processing and other recently. emerged special technologies. Promising new trends and future directions in image analysis. A lavish 16-page color insert. . Inside this Expert Guide to Analyzing Remotely Sensed Images. Remotely sensed data Storage of remotely sensed data The image analysis system Image rectification Image enhancement Image classification Accuracy assessment Change detection Intelligent image classification Decision tree classification Innovative image classification Integration of image analysis with GIS/GPS . .
Cited By
- Song C, Li Z, Xu W, Zhou C, Jin Z and Ren K (2018). My Smartphone Recognizes Genuine QR Codes!, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2:2, (1-20), Online publication date: 5-Jul-2018.
- (2017). Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery, Computers & Geosciences, 103:C, (1-11), Online publication date: 1-Jun-2017.
Index Terms
- Digital Analysis of Remotely Sensed Imagery
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