Spatial Reasoning in Remotely Sensed Data

High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization

R sensed data have been employed for the characterization of ecologically important variables from local through global contexts. These data may be used to generate a wide range of estimates that are valuable to ecologists, including information on land cover, vegetation cover, habitat, forest structure, and forest function (Kerr and Ostrovsky 2003), and to track changes in these variables. Rec...

Remotely Sensed Data Segmentation under a Spatial Statistics Framework

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pi...

248 Remotely Sensed Data Characterization

EMPs Extended morphological profiles EMPs Extended morphological profiles LDA Linear discriminant analysis LogDA Logarithmic discriminant analysis MLR Multinomial logistic regression MLRsubMRF Subspace-based multinomial logistic regression followed by Markov random fields MPs Morphological profiles MRFs Markov random fields PCA Principal component analysis QDA Quadratic discriminant analysis RH...

Commercial Remotely Sensed (CRS) Data

Natural disasters can severely impact transportation networks. In the hours and days following a major flooding event, knowing the location and extent of the damage is crucial for incident managers for a number of reasons: it allows for emergency vehicle access to affected areas; it facilitates the efficient rerouting of traffic; it raises the quality and reduces the cost of repairs; and it all...