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Displaying 496 - 500 of 661Object-oriented classification of land use/cover using digital aerial orthophotography
In automatic/semiautomatic mapping of land use/cover using very high resolution remote-sensing imagery, the major challenge is that a single class of land use contains ground targets with varied spectral values, textures, geometries and spatial features. Here we present an object-oriented strategy for automatic/semiautomatic classifications of land use/cover using very high resolution remote-sensing data. The strategy consists of character detecting, object positioning and coarse classification, then refining the classification result step by step.
Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series
Monitoring land cover over large areas on a yearly basis is challenging. The spatial and temporal consistency of an object-based change detection algorithm was tested through a multi-year application on the forest of Borneo, using SPOT-VEGETATION time series from 2000 to 2008. Continuous change thresholds allowed the tuning of the algorithm according to specific requirements in terms of omission and commission errors.
Land cover in Upper Egypt assessed using regional and global land-cover products derived from MODIS imagery
Irrigation along the Nile River has resulted in dramatic changes in the biophysical environment of Upper Egypt. In this study we used a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m Normalized Difference Vegetation Index data and Landsat imagery to identify areas that changed from 2001 to 2008 as a result of irrigation and water-level fluctuations in the Nile River and nearby waterbodies.
comparative analysis of spatial indices and wavelet-based classification
Spatial indices measure the geometric arrangement of land use and land cover classes at various scales and are computationally adaptive with wavelet transform coefficients. Decision rules built on permutations of three spatial indices – energy, log energy and Shannon's diversity – are used to improve the accuracy of multi-resolution hierarchical wavelet-based classifications. Comparisons are made with classification results derived from other texture measures, as well as with classification results calculated from more conventional per-pixel techniques.
Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change
The ground reference data obtained to assess map accuracy can be simultaneously used to estimate area (extent). This dual-purpose use of ground reference data is examined for the special case of a two-class map of ‘change’ and ‘no change’. To assess the accuracy of a change map, stratified sampling is often implemented with a disproportionately larger sample size allocated to the map change stratum. But this allocation targeting user's accuracy of change is not necessarily effective for the competing objective of estimating the area of change.