Computer processing of remotely sensed images

By Ali Jawad. 2005

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A remote sensing method for resolving depth and subpixel composition of aquatic benthos (2003, background): “A second fundamental problem in remote sensing is the existence of substrate heterogeneity at subpixel scales (Mather 1999).”
Landscape-level interactions of prefire vegetation, burn severity, and postfire vegetation over a 16-year period in interior Alaska (2005, unknown): “For both dates, an unsupervised classification was conducted using the ISODATA algorithm (Mather 1987), with 128 spectral classes.”
A relative evaluation of multiclass image classification by support vector machines (2004, background): “Consequently, it is often recommended that the training set comprise, as a minimum, a sample of typically 10–30 pixels per class per waveband used [32].”
Improved forest biomass estimates using ALOS AVNIR-2 texture indices (2011, background): “A vegetation index simply expresses the image values observed in two or more wavebands as a single value that is related to the biophysical variable of interest (Mather, 1999).”
Geometric correction accuracy of different satellite sensor images: application of figure condition (2007, methodology): “Without accurate orientation parameters for the sensor platform, these errors can only be corrected with the use of ground control points (GCPs) and a suitable precision photogrammetric or empirical model (Jensen 1996 , Mather 1999 ).”