Five different types of mixture models are reviewed. These are: linear, probabilistic,
geometric‐optical, stochastic geometric, and fuzzy models. A summary of the conception …

Remotely sensed surface parameters, such as vegetation index, leaf area index, surface
temperature, and evapotranspiration, show diverse spatial scales and temporal dynamics …

Spatiotemporal image fusion is a potential way to resolve the constraint between the spatial
and temporal resolutions of satellite images and has been developed rapidly in recent …

Constrained and unconstrained algorithms of the multisensor multiresolution technique
(MMT) are discussed. They can be applied to unmix low-resolution images using the …

Evidence of the rapid losses of coastal marshes calls for the application of remote sensing
data. Nonetheless, many features indicative of incipient marsh loss, such as widening of …

Current satellite remote-sensing systems compromise between spatial resolution and
spectral and/or temporal resolution, which potentially limits the use of remotely sensed data …

Both signal processing and image processing are playing increasingly important roles in
remote sensing. As most data from satellites are in image form, image processing has been …

Spectral mixture modeling was performed for identification and map** of land degradation
features related to soil erosion processes in the Sacaba Valley, Bolivia. The model allowed …

We present a technique for extracting subpixel cover type reflectances from the mixed pixels
of a coarse spatial resolution image. A Gaussian filter is used to spatially degrade a set of …

Several earth observation satellites acquire image bands with different spatial resolutions,
eg, a panchromatic band with high resolution and spectral bands with lower resolution …