Siamak Khorram

Processing and Applications of Remotely Sensed Data

Khorram, S., Nelson, S. A. C., Wiele, C. F., & Cakir, H. (2017, January 1). HANDBOOK OF SATELLITE APPLICATIONS,2ND EDITION, pp. 1017–1046.

Basal Area and Biomass Estimates of Loblolly Pine Stands Using L-band UAVSAR

Marks, W. L., Iiames, J. S., Lunetta, R. S., Khorram, S., & Mace, T. H. (2013, December 24). Photogrammetric Engineering & Remote Sensing.

High-resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning

International Journal of Remote Sensing, 31(2), 455–475.

An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery

Yuan, H., Wiele, C. F. V. D., & Khorram, S. (2009, July 9). Remote Sensing.

Global registration of overlapping images using accumulative image features

Pattern Recognition Letters, 31(2), 112–118.

Per-pixel Classification of High Spatial Resolution Satellite Imagery for Urban Land-cover Mapping

Hester, D. B., Cakir, H. I., Nelson, S. A. C., & Khorram, S. (2008, April 1). Photogrammetric Engineering & Remote Sensing, Vol. 74, pp. 463–471.

Pixel Level Fusion of Panchromatic and Multispectral Images Based on Correspondence Analysis

Cakir, H. I., & Khorram, S. (2008, February 1). Photogrammetric Engineering & Remote Sensing.

Correspondence analysis for detecting land cover change

Remote Sensing of Environment, 102(3-4), 306–317.

Integration of high resolution imagery in cost-effective assessment of land use practices influencing erosion and sediment yield

In Water Resource Research Institute final report (Center for Earth Observation Technical Report) (Vol. 221).

Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach

Knight, J. F., Lunetta, R. S., Ediriwickrema, J., & Khorram, S. (2006, March 1). GIScience & Remote Sensing.

Integration of high resolution imagery in cost-effective assessment of land use practices influencing erosion and sediment yield

In Water Resource Research Institute final report (Center for Earth Observation Technical Report) (Vol. 221).

Accuracy assessment curves for satellite-based change detection

Photogrammetric Engineering and Remote Sensing, 66(7), 875–880.

A feature-based image registration algorithm using improved chain-code representation combined with invariant moments

Dai, N. X., & Khorram, S. (1999, January 1). IEEE Transactions on Geoscience and Remote Sensing.

Data fusion using artificial neural networks: a case study on multitemporal change analysis

Dai, X., & Khorram, S. (1999, January 1). Computers Environment and Urban Systems.

Land-cover change detection enhanced with generalized linear models

Morisette, J. T., Khorram, S., & Mace, T. (1999, January 1). International Journal of Remote Sensing.

Remotely sensed change detection based on artificial neural networks

Photogrammetric Engineering and Remote Sensing, 65(3), 1187–1194.

Roadway feature extraction and delineation fron high-resolution satellite imagery

EOM, (1999 May), 34–37.

Techniques for Automated Extraction of Roadway Inventory Features from High‐Resolution Satellite Imagery

Karimi, H. A., Dai, X., Khorram, S., Khattak, A. J., & Hummer, J. E. (1999, June 1). Geocarto International.

A hierarchical methodology framework for multisource data fusion in vegetation classification

Dai, X., & Khorram, S. (1998, December 1). International Journal of Remote Sensing.

Exact binomial confidence interval for proportions

Photogrammetric Engineering and Remote Sensing, 64(4), 281–283.

The effects of image misregistration on the accuracy of remotely sensed change detection

Dai, N. X., & Khorram, S. (1998, January 1). IEEE Transactions on Geoscience and Remote Sensing.

Hierarchical maximum-likelihood classification for improved accuracies

Ediriwickrema, J., & Khorram, S. (1997, July 1). IEEE Transactions on Geoscience and Remote Sensing.

Forest decline model development with LANDSAT-TM, SPOT, DEM DATA

IEEE Transactions on Geoscience and Remote Sensing, 29, 459–466.

Modeling And Multitemporal Evaluation Of Forest Decline With Landsat Tm Digital Data

Khorram, S., Brockhaus, J. A., Bruck, R. I., & Campbell, M. V. (1990, July 1). IEEE Transactions on Geoscience and Remote Sensing.

Analysis of forest decline in the Southern Appalachian Mountains.

Proceedings of the American Society of Photogrammetry and Remote Sensing, 41, 419–429.

Multi-temporal modeling of forest decline from Landsat TM digital data

Proceedings of the International Society of Remote Sensing, 37, 771–779.

Observations of forest decline in the boreal montane ecosystems of Mt. Mitchell, N.C.

Proceedings of the U.S.-F.R.G. Symposium on Forest Decline, Burlington, VT, Oct. 19-24, 1987 (USDA Forest Service Technical publication #120), 97–107. USDA Forest Service.

The effect of field plot location errors within TM data on forest decline model development

Proceedings of the American Society of Photogrammetry and Remote Sensing, 41, 430–437.

Comparison of Landsat MSS and TM data for urban land use classification

In Comparison of Landsat MSS and TM data for urban land use classification (p. 23). Raleigh, N.C.: NCSU School of Forest Resources.