Works (29)

Updated: August 16th, 2024 13:38

2017 article

Processing and Applications of Remotely Sensed Data

HANDBOOK OF SATELLITE APPLICATIONS,2ND EDITION, pp. 1017–1046.

By: S. Khorram*, S. Nelson n, C. Wiele* & H. Cakir*

author keywords: Digital image processing; Supervised classifiers; Unsupervised classifiers; Filtering; Accuracy assessment classification schemes; Geospatial modeling; Image validation; Image visualization; Post-processing; Satellite remote sensing geospatial modeling
TL;DR: Digital image processing, post-processing, and data integration techniques as applied to airborne and satellite remotely sensed data for the purpose of extracting useful Earth resources information will be discussed in this chapter. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: December 31, 2018

2013 journal article

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

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 80(1), 33–42.

By: W. Marks, J. Iiames, R. Lunetta, S. Khorram* & T. Mace

UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

2010 journal article

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

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

By: D. Hester n, S. Nelson n, H. Cakir n, S. Khorram n & H. Cheshire n

TL;DR: This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and practical change analysis and creates a continuous representation of change, a product type that retains more information and flexibility than discrete maps of change. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Sources: Web Of Science, Crossref, NC State University Libraries
Added: August 6, 2018

2009 journal article

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

REMOTE SENSING, 1(3), 243–265.

By: H. Yuan, C. Van Der Wiele n & S. Khorram n

author keywords: automated artificial neural network; simulated annealing; Kohonen's self-organizing mapping; Landsat TM; land use land cover; image classifiers; image processing; accuracy assessment
TL;DR: It is concluded that the automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2009 journal article

Global registration of overlapping images using accumulative image features

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

author keywords: Image registration; Feature matching; Accumulator-based methods; Feature correspondence; Evidence accumulation
TL;DR: A new feature-based image registration technique which registers images by finding rotation- and scale-invariant features and matching them using a novel feature matching algorithm based on an evidence accumulation process reminiscent of the generalized Hough transform is introduced. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, Crossref
Added: August 6, 2018

2008 journal article

Per-pixel classification of high spatial resolution satellite imagery for urban land-cover mapping

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 74(4), 463–471.

By: D. Hester, H. Cakir*, S. Nelson* & S. Khorram*

TL;DR: These findings demonstrate that conventional spectral-based classification methods can be used to generate highly accurate maps of urban landscapes using high spatial resolution imagery. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2008 journal article

Pixel level fusion of panchromatic and multispectral images based on correspondence analysis

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 74(2), 183–192.

By: H. Cakir* & S. Khorram*

TL;DR: A pixel level data fusion approach based on correspondence analysis (CA) is introduced for high spatial and spectral resolution satellite data and provided an excellent spectral accuracy when synthesizing images from multispectral and high spatial resolution panchromatic imagery. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2006 journal article

Correspondence analysis for detecting land cover change

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

By: H. Cakir n, S. Khorram n & S. Nelson n

author keywords: correspondence analysis; LULC change detection; principal component analysis; normalized difference vegetation index
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Sources: Web Of Science, NC State University Libraries, Crossref
Added: August 6, 2018

2006 report

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).

By: D. Hester, H. Cakir, S. Nelson & S. Khorram

Source: NC State University Libraries
Added: August 6, 2018

2006 journal article

Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology-based approach

GISCIENCE & REMOTE SENSING, 43(1), 1–23.

By: J. Knight*, R. Lunetta*, J. Ediriwickrema & S. Khorrarn

UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

2005 report

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).

By: S. Khorram, S. Nelson, H. Cakir & D. Hester

Source: NC State University Libraries
Added: August 6, 2018

2000 journal article

Accuracy assessment curves for satellite-based change detection

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

By: J. Morisette & S. Khorram

Source: NC State University Libraries
Added: August 6, 2018

1999 journal article

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

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 37(5), 2351–2362.

By: X. Dai n & S. Khorram n

author keywords: automated; feature extraction; image matching; image registration; satellite imagery
TL;DR: A new feature-based approach to automated image-to-image registration that combines an invariant-moment shape descriptor with improved chain-code matching to establish correspondences between the potentially matched regions detected from the two images is presented. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

1999 journal article

Data fusion using artificial neural networks: A case study on multitemporal image analysis

Computers, Environment and Urban Systems, 23(2), 19–31.

By: X. Dai n & S. Khorram n

TL;DR: A formulation framework for data fusion in land cover characterization and a case study on multitemporal change analysis using artificial neural networks, which finds the neural network-based approach to multitem temporal change analysis to be one of the promising methods. (via Semantic Scholar)
UN Sustainable Development Goal Categories
15. Life on Land (OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018

1999 journal article

Land-cover change detection enhanced with generalized linear models

INTERNATIONAL JOURNAL OF REMOTE SENSING, 20(14), 2703–2721.

By: J. Morisette*, S. Khorram* & T. Mace

TL;DR: This paper explores the use of generalized linear models (GLMs) for enhancing standard methods of satellite-based land-cover change detection using a change detection over two locations in North Carolina, USA, and shows how the models provide a quantitative approach to image-based change detection. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

1999 journal article

Remotely sensed change detection based on artificial neural networks

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

By: X. Dai & S. Khorram

Source: NC State University Libraries
Added: August 6, 2018

1999 journal article

Roadway feature extraction and delineation fron high-resolution satellite imagery

EOM, (1999 May), 34–37.

By: X. Dai, H. Karimi, S. Khorram, A. Khattak & J. Hummer

Source: NC State University Libraries
Added: August 6, 2018

1999 journal article

Techniques for automated extraction of roadway inventory features from high-resolution satellite imagery

Geocarto International, 14(2), 5–16.

By: H. Karimi*, X. Dai n, A. Khattak n, S. Khorram n & J. Hummer n

Source: NC State University Libraries
Added: August 6, 2018

1998 journal article

A hierarchical methodology framework for multisource data fusion in vegetation classification

INTERNATIONAL JOURNAL OF REMOTE SENSING, 19(18), 3697–3701.

By: X. Dai* & S. Khorram*

TL;DR: This Letter presents a new methodological framework for a hierarchical data fusion system for vegetation classification using multi-sensor and multitemporal remotely sensed imagery that is built upon a hierarchy of vegetation canopy attributes that can be remotely detected by sensors. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

1998 journal article

Exact binomial confidence interval for proportions

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

By: J. Morisette & S. Khorram

Source: NC State University Libraries
Added: August 6, 2018

1998 article

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

Dai, X. L., & Khorram, S. (1998, September). IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 36, pp. 1566–1577.

By: X. Dai n & S. Khorram n

author keywords: accuracy assessment; change detection; false change analysis; image registration; remotely sensed
TL;DR: The results from false change analysis indicate a substantial degradation in the accuracy of remotely sensed change detection due to misregistration, and it is shown that a registration accuracy of less than one-fifth of a pixel is required to achieve a change detection error ofLess than 10%. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

1997 article

Hierarchical maximum-likelihood classification for improved accuracies

Ediriwickrema, J., & Khorram, S. (1997, July). IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 35, pp. 810–816.

By: J. Ediriwickrema* & S. Khorram*

TL;DR: This study has explored a hierarchical pixel classification (HPC) method to estimate prior probabilities for the spectral classes from the Landsat thematic mapper (TM) data and spectral signatures and showed increased accuracy over three classification methods. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

1991 journal article

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

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

By: J. Brockhaus, M. Campbell, S. Khorram, R. Bruck & C. Stallings

Source: NC State University Libraries
Added: August 6, 2018

1990 article

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). IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 28, pp. 746–748.

By: S. Khorram*, J. Brockhaus*, R. Bruck* & M. Campbell*

UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: August 6, 2018

1989 journal article

Analysis of forest decline in the Southern Appalachian Mountains.

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

By: J. Brockhaus, M. Campbell, R. Bruck & S. Khorram

Source: NC State University Libraries
Added: August 6, 2018

1989 journal article

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

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

By: S. Khorram, J. Brockhaus, R. Bruck & M. Campbell

Source: NC State University Libraries
Added: August 6, 2018

1989 conference paper

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.

By: R. Bruck, R. Bradow, J. Brockhaus, B. Cure, S. Khorram, A. McDaniel, S. Modena, W. Robarge, P. Smithson

Source: NC State University Libraries
Added: August 6, 2018

1989 journal article

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.

By: M. Campbell, J. Brockhaus, R. Bruck & S. Khorram

Source: NC State University Libraries
Added: August 6, 2018

1988 book

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.

By: S. Khorram, J. Brockhaus & H. Cheshire

Source: NC State University Libraries
Added: August 6, 2018

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