comparison of methods for the retrieval of surface reflectance factor from multitemporal

Monitoring the maximum turbidity zone and detecting fine-scale turbidity features in the Gironde estuary using high spatial resolution

Abstract Mangrove forests in the western

Almost all forests in Japan are located on sloping ground in rugged terrain. Shade caused by such topographic relief can create serious obstacles to the analysis of remote sensing data (Leprieur et al., 1988; Civco, 1989). Since the early 1980s, various forest remote sensing studies have attempted to address this “topographic effect ” problem (e.g., Holben and Justice, 1980; Smith et al., 1980). Two correction methods are available to offset topographic effects: a non-geometric technique that uses band ratios and a geometric technique based on solar, surface, and sensor positions. The present study focused on the geometric technique. Smith et al. (1980) introduced geometric correction methods for topographic effects. The Lambert model and model correction methods that they presented continue to be cited by many researchers and a number of verification studies have been conducted (e.g., Teillet et al., 1982; Chiou et

Abstract. The contribution of automatically processed Système Pour l’Observation de la Terre (SPOT) high resolution visible (HRV) Pan data as an ancillary source of information to a digital elevation model (DEM)-based method for channel network extraction is introduced. The image processing stage

In view of multiple sensor approaches in remote sensing, we have analyzed the geometric and, in particular, radiometric accuracy of multispectral SPOT HRV (high resolution visible) data in com-parison to Landsat-5 Thematic Mapper images. We used concurrent system corrected TM and SPOT images which

I n recent years, there have been many land-surface studies based on visible and near-infrared reflectance values retrieved from the Landsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV) sensors. Re-trieval of reflectance from satellite sensor digital count requires knowledge

: In remote sensing there is usually a trade-off between spatial resolution and spectral resolution. It is often desirable to present a combination of the data which merges the low resolution spectral (colour) information with the high resolution spatial information. If the ratio in spatial resolution between the greyscale image and the colour image becomes too great the image resulting from usual itensity substitution techniques will tend to look as if it has "squares" of colour information overlaid on the greyscale image. A new way of "fusing" the colour information with the greyscale information is presented here. The technique is based on Markov random field (MRF) assumptions and the maximum a posteriori (MAP) estimate is found by means of iterated conditional modes (ICM). The design of the specific Markov random field used here allows the user to control three properties of the resulting "merged" image: 1) degree of colour smoothness between neighbouring pixels, 2) degree of confi...

approximate 70 % of delta’s surface. The study is based on two HRV SPOT images, acquired in

Abstract: An integrated method that incorporates the advantages of per-parcel and perpixel approaches as well as spectral and spatial characteristics was proposed for crop classification of a typical agricultural area in south-east China using SPOT5 HRV data. The co-occurrence texture was employed