Mr. George P. Lemeshewsky Profile

George P. Lemeshewsky

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Publications (8)

Proceedings Article | 25 May 2005 Paper

Sharpening advanced land imager multispectral data using a sensor model

George Lemeshewsky

Proceedings Volume 5817, (2005) https://doi.org/10.1117/12.604517

KEYWORDS: Point spread functions, Image resolution, Sensors, Alternate lighting of surfaces, Spatial resolution, Earth observing sensors, Image fusion, High resolution satellite images, Image sensors, Image processing

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Proceedings Article | 15 July 2004 Paper

Visual enhancement of unmixed multispectral imagery using adaptive smoothing

George Lemeshewsky

Proceedings Volume 5438, (2004) https://doi.org/10.1117/12.543109

KEYWORDS: Image segmentation, Visualization, Image processing, Image enhancement, Landsat, Composites, Sensors, RGB color model, Multispectral imaging, Earth observing sensors

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Proceedings Article | 8 August 2003 Paper

Landsat multispectral sharpening using a sensor system model and panchromatic image

George Lemeshewsky

Proceedings Volume 5108, (2003) https://doi.org/10.1117/12.487262

KEYWORDS: Sensors, Point spread functions, Spatial resolution, Data modeling, RGB color model, Image sensors, Landsat, Spectral resolution, Visible radiation, Earth observing sensors

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Proceedings Article | 30 July 2002 Paper

Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges

George Lemeshewsky

Proceedings Volume 4736, (2002) https://doi.org/10.1117/12.477580

KEYWORDS: Image fusion, Discrete wavelet transforms, Image processing, Near infrared, Wavelets, Spatial resolution, Image resolution, RGB color model, Multispectral imaging, Wavelet transforms

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Proceedings Article | 28 August 2001 Paper

Landsat 7 thermal-IR image sharpening using an artificial neural network and sensor model

George Lemeshewsky, Robert Schowengerdt

Proceedings Volume 4388, (2001) https://doi.org/10.1117/12.438256

KEYWORDS: Sensors, Spatial resolution, Landsat, Spatial filters, Image resolution, Data modeling, Image sensors, Gaussian filters, Image enhancement, Earth observing sensors

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The enhanced thematic mapper (plus)(ETM+) instrument on Landsat 7 shares the same basic design as the TM sensors on Landsats 4 and 5, with some significanti mprovements. In common are six multispetral bands with a 30-m ground projected instantaneous field of view (GIFOV). However, the thermal-IR (TIR) band now has a 60-m GIFOV, instead of 120- m. Also, a 15-m panchromatic band has been added. The artificial neural network (NN) image sharpening method described here uses data form the higher spatial resolution ETM+ bands to enhance (sharpen) the spatial resolution of the TIR imagery. It is based on an assumed correlation over multiple scales of resolution, between image edge contrast patterns in the TIR band and several other spectral bands. A multilayer, feedforward NN is trained to approximate TIR data at 60m, given degraded (from 30-m to 60-m) spatial resolution input from spectral bands 7,5, and 2. After training, the NN output for full-resolution input generates an approximation of a TIR image at 30-m resolution. Two methods are used to degrade the spatial resolution of the imagery used for NN training, and the corresponding sharpening results are compared. One degradation method uses a published sensor transfer function (TR) for Landsat 5 to simulate sensor coarser resolution imagery form higher resolution imagery. For comparison, the second degradation method is simply Gaussian low pass filtering and subsampling, wherein the Gaussian filter approximates the full width at half maximum amplitude characteristics of the TF-based spatial filter. Two fixed-size Nns (that is, number of weights and processing elements) were trained separately with the degraded resolution data, and the sharpening results compared. The comparison evaluates the relative influence of the degradation technique employed and whether or not it is desirable to incorporate a sensor TF model. Preliminary results indicate some improvements for the sensor model-based technique. Further evaluation using a higher resolution reference image and strict application of sensor model to data is recommended.

Proceedings Article | 23 August 2000 Paper

Multispectral image sharpening using wavelet transform techniques and spatial correlation of edges

George Lemeshewsky, Robert Schowengerdt

Proceedings Volume 4049, (2000) https://doi.org/10.1117/12.410376

KEYWORDS: Image fusion, Discrete wavelet transforms, Image resolution, Image processing, Wavelets, Image filtering, Spatial resolution, Near infrared, Wavelet transforms, Multispectral imaging

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Proceedings Article | 21 July 1999 Paper

Multispectral multisensor image fusion using wavelet transforms

George Lemeshewsky

Proceedings Volume 3716, (1999) https://doi.org/10.1117/12.354709

KEYWORDS: Image fusion, Discrete wavelet transforms, Wavelets, Image resolution, Spatial resolution, Image processing, RGB color model, Data fusion, Multispectral imaging, Composites

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Proceedings Article | 6 July 1998 Paper

Neural network-based sharpening of Landsat thermal-band images

George Lemeshewsky

Proceedings Volume 3387, (1998) https://doi.org/10.1117/12.316428

KEYWORDS: Spatial resolution, Image resolution, Neural networks, Data analysis, Point spread functions, Image processing, Landsat, Sensors, Vegetation, Earth observing sensors

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