1,721,008 research outputs found
Predicting missing field boundaries to increase per-field classification accuracy
Full text linkWith the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in" monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.)
Identifying missing field boundaries to increase the accuracy of per-field classification of fine spatial resolution satellite sensor imagery
No full textCombining fuzzy and per-parcel classification techniques to map urban land cover from fine spatial resolution satellite sensor imagery
No full textSub-pixel land cover mapping for per-field classification
No full textA method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a per-field classification based on hard per-pixel classified imager
Spatial variation in land cover and choice of spatial resolution for remote sensing
No full textPrior to acquiring remotely sensed imagery with which to map land cover investigators may wish to select an appropriate spatial resolution. Previously, statistics such as the local variance and scale variance have been used to facilitate this goal. However, where such statistics vary locally over the region of interest, their use in selecting a single spatial resolution may be undermined. The variogram and scale variance (plotted as a function of spatial resolution) were predicted for airborne multispectral imagery with a spatial resolution of 4 m of St Albans, Hertfordshire, UK and of Arundel, Sussex, UK. The remotely sensed response in the red and near-infrared wavelengths was found to vary appreciably both within and between broad land categories (such as urban, agricultural and semi-natural areas). These differences mean that where the subject of interest is a general region rather than a specific feature or object the mean local variance or scale variance over that region may be unhelpful in selecting a single spatial resolution. Further, differences observed between the red and near-infrared wavelengths have implications for users who wish to select a single spatial resolution for multispectral imagery
Integrating fine spatial resolution satellite sensor imagery and digital vector data for per-field cover mapping
No full textUsing the spectral properties of fine spatial resolution satellite sensor imagery for national land cover and land use mapping
No full textEvaluating the potential of the forthcoming commercial US high-resolution satellite sensor imagery at the Ordnance Survey
No full textAs the National Mapping Agency of Great Britain, the Ordinance Survey® (OS) is driven by a need to reduce costs and commercialize operations, and as such has been investigating photogrammetric methods to improve existing products, streamline existing production, and increase the current portfolio of products. Over the last 18 months, the OS has been involved in a major research project to tackle these issues through an evaluation of the forthcoming commercial U.S. high spatial resolution satellite sensors which are offering 1-m panchromatic and 4-m multispectral spatial resolutions. Work has focused on improving the existing National Height Dataset (NHD), reducing the cost of photogrammetric survey, automatic topographic feature change detection, production of DEMs; three-dimensional (3D) urban models, and land-use classification. Results from the project using simulated imagery indicate that it would have potential within the OS in all areas evaluated. The work now needs to be followed up when real high spatial resolution satellite imagery becomes commercially available
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