Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper...

As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, ...

Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optic...

In this paper, we explore the potential of remote sensing to map pastures areas and by this way establish models for predicting cattle density and location. First, an object based classification (OB) was made in Landsat 5 images for three different municipalities to provide a land-cover map. Second, on the basis of Brazilian official livestock database, a statistical model to predict number of ...

A methodology for image fusion based on the Hermite transform is presented. First, we demonstrate fusion with multispectral images from the same satellite (Landsat 7 ETM+) with different spatial resolutions. In this case we show how the proposed method can help improve spatial resolution. In the second case we show fusion with different sensor images, namely SAR and multispectral Landsat 5 TM. ...

This study aimed to evaluate the response of the Normalized Difference Vegetation Index NDVI derived from Terra MODIS sensor to soybean phenology, in a grain producer region of Parana State, Brazil. Landsat TM and ETM+ images were selected to analyze the spatial distribution of soybean fields for this region from 2000/01 to 2006/07 crop seasons. Then pure pixel samples (250 x 250m) that contain...

Multitemporal satellite optical images of the same terrain are very applicable in monitoring and quantifying large scale land cover change over time. These images are confounded in terms of radiometric consistency due to differences in sensor calibration parameters, illumination, geometric condition and variation in atmospheric effects. To analyze these images, it is necessary to omit mentioned...