@article{khorram_nelson_wiele_cakir_2017, title={Processing and Applications of Remotely Sensed Data}, ISBN={["978-3-319-23385-7"]}, DOI={10.1007/978-3-319-23386-4_92}, journal={HANDBOOK OF SATELLITE APPLICATIONS,2ND EDITION}, author={Khorram, Siamak and Nelson, Stacy A. C. and Wiele, Cynthia F. and Cakir, Halil}, year={2017}, pages={1017–1046} }
 @article{marks_iiames_lunetta_khorram_mace_2014, title={Basal Area and Biomass Estimates of Loblolly Pine Stands Using L-band UAVSAR}, volume={80}, ISSN={["2374-8079"]}, DOI={10.14358/pers.80.1.33}, abstractNote={Fully polarimetric L-band Synthetic Aperture Radar ( SAR ) backscatter was collected using NASA ’s Unmanned Aerial Vehicle ( UAV ) SAR and regressed with in situ measurements of basal area ( BA ) and above ground biomass ( AGB ) of mature loblolly pine stands in North Carolina. Results found HH polarization consistently displayed the lowest correlations where HV and VV exhibited the highest correlations in all groups for both BA and AGB . When plantation stands were analyzed separately (plantation versus natural), correlation improved signifi cantly for both BA (R 2 = 0.65, HV ) and AGB (R 2 = 0.66, VV ). Similarly, results improved when natural stands were analyzed separately resulting in the highest correlation for AGB (R 2 = 0.63, HV and VV ). Data decomposition using the Freeman 3-component model indicated that the relative low correlations were due to the saturation of the L-band backscatter across the majority of the study area.}, number={1}, journal={PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING}, author={Marks, William L. and Iiames, John S. and Lunetta, Ross S. and Khorram, Siamak and Mace, Thomas H.}, year={2014}, month={Jan}, pages={33–42} }
 @article{krish_heinrich_snyder_cakir_khorram_2010, title={Global registration of overlapping images using accumulative image features}, volume={31}, ISSN={0167-8655}, url={http://dx.doi.org/10.1016/j.patrec.2009.09.016}, DOI={10.1016/j.patrec.2009.09.016}, abstractNote={This paper introduces 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. Once feature correspondence has been established, the transformation parameters are then estimated using non-linear least squares (NLLS) and the standard RANSAC (random sample consensus) algorithm. The technique is evaluated under similarity transforms – translation, rotation and scale (zoom) and also under illumination changes.}, number={2}, journal={Pattern Recognition Letters}, publisher={Elsevier BV}, author={Krish, Karthik and Heinrich, Stuart and Snyder, Wesley E. and Cakir, Halil and Khorram, Siamak}, year={2010}, month={Jan}, pages={112–118} }
 @article{hester_nelson_cakir_khorram_cheshire_2010, title={High-resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning}, volume={31}, ISSN={0143-1161 1366-5901}, url={http://dx.doi.org/10.1080/01431160902893493}, DOI={10.1080/01431160902893493}, abstractNote={Land cover change detection is an important research and application area for analysts of remote sensing data. The primary objective of the research described here was to develop a change detection method capable of accommodating spatial and classification uncertainty in generating an accurate map of land cover change using high resolution satellite imagery. As a secondary objective, this method was designed to facilitate the mapping of particular types and locations of change based on specific study goals. Urban land cover change pertinent to surface water quality in Raleigh, North Carolina, was assessed using land cover classifications derived from pan-sharpened, 0.61 m QuickBird images from 2002 and 2005. Post-classification map errors were evaluated using a fuzzy logic approach. First, a ‘change index’ representing a quantitative gradient along which land cover change is characterized by both certainty and relevance, was created. The result was a continuous representation of change, a product type that retains more information and flexibility than discrete maps of change. Finally, fuzzy logic and change reasoning results were integrated into a binary change/no change map that quantified the most certain, likely, and relevant change regions within the study area. A ‘from-to’ change map was developed from this binary map inserting the type of change identified in the raw post-classification map. A from-to change map had an overall accuracy of 78.9% (κ = 0.747) and effectively mapped land cover changes posing a threat to water quality, including increases in impervious surface. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and practical change analysis.}, number={2}, journal={International Journal of Remote Sensing}, publisher={Informa UK Limited}, author={Hester, D. B. and Nelson, S. A. C. and Cakir, H. I. and Khorram, S. and Cheshire, H.}, year={2010}, month={Jan}, pages={455–475} }
 @article{yuan_van der wiele_khorram_2009, title={An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery}, volume={1}, ISSN={["2072-4292"]}, DOI={10.3390/rs1030243}, abstractNote={This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA). The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC) classifications of a Landsat Thematic Mapper (TM) image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR) Digital Orthophoto Quarter Quad (DOQQ) data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our 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.}, number={3}, journal={REMOTE SENSING}, author={Yuan, Hui and Van Der Wiele, Cynthia F. and Khorram, Siamak}, year={2009}, month={Sep}, pages={243–265} }
 @article{hester_cakir_nelson_khorram_2008, title={Per-pixel classification of high spatial resolution satellite imagery for urban land-cover mapping}, volume={74}, ISSN={["2374-8079"]}, DOI={10.14358/PERS.74.4.463}, abstractNote={Commercial high spatial resolution satellite data now provide a synoptic and consistent source of digital imagery with detail comparable to that of aerial photography. In the work described here, per-pixel classification, image fusion, and GIS-based map refinement techniques were tailored to pan-sharpened 0.61 m QuickBird imagery to develop a six-category urban land-cover map with 89.3 percent overall accuracy ( �� 0.87). The study area was a rapidly developing 71.5 km 2 part of suburban Raleigh, North Carolina, U.S.A., within the Neuse River basin. “Edge pixels” were a source of classification error as was spectral overlap between bare soil and impervious surfaces and among vegetated cover types. Shadows were not a significant source of classification error. 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.}, number={4}, journal={PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING}, author={Hester, David Barry and Cakir, Halil I. and Nelson, Stacy A. C. and Khorram, Siamak}, year={2008}, month={Apr}, pages={463–471} }
 @article{cakir_khorram_2008, title={Pixel level fusion of panchromatic and multispectral images based on correspondence analysis}, volume={74}, ISSN={["2374-8079"]}, DOI={10.14358/PERS.74.2.183}, abstractNote={A pixel level data fusion approach based on correspondence analysis (CA) is introduced for high spatial and spectral resolution satellite data. Principal component analysis (PCA) is a well-known multivariate data analysis and fusion technique in the remote sensing community. Related to PCA but a more recent multivariate technique, correspondence analysis, is applied to fuse panchromatic data with multispectral data in order to improve the quality of the final fused image. In the CA-based fusion approach, fusion takes place in the last component as opposed to the first component of the PCA-based approach. This new approach is then quantitatively compared to the PCA fusion approach using Landsat ETM� , QuickBird, and two Ikonos (with and without dynamic range adjustment) test imagery. The new approach provided an excellent spectral accuracy when synthesizing images from multispectral and high spatial resolution panchromatic imagery.}, number={2}, journal={PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING}, author={Cakir, Halil I. and Khorram, Siamak}, year={2008}, month={Feb}, pages={183–192} }
 @article{cakir_khorram_nelson_2006, title={Correspondence analysis for detecting land cover change}, volume={102}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/j.rse.2006.02.023}, DOI={10.1016/j.rse.2006.02.023}, abstractNote={The correspondence analysis (CA) method was applied to two multitemporal Landsat images of Raleigh, North Carolina for land use land cover (LULC) change detection. After the spectral transformation of the individual date images into component space using CA, the first component (PC1) of the date 1 image was subtracted from the PC1 of the date 2 image to produce difference image highlighting change areas. Accuracy curves based on the cumulative Producer's and User's accuracies were then used to optimally locate threshold (cutoff) values in the high-end and low-end tails of the difference image's histogram. Results were then compared to the standardized and non-standardized Principal Component Analysis (PCA) differencing and Normalized Difference Vegetation Index (NDVI) differencing methods for change detection. Results showed that there was 6.8% increase in urban related cover types in Raleigh metropolitan area between 1993 and 1999. Also, maps based on the CA differencing method were found to be thematically more accurate than maps based on PCA component differencing methods. Overall accuracy of change map produced by the CA method for the Raleigh metropolitan area was 92.5% with overall Kappa value of 0.88. In general, CA was found to be a powerful multivariate analysis technique when applied to change detection.}, number={3-4}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Cakir, Halil Ibrahim and Khorram, Siamak and Nelson, Stacy A.C.}, year={2006}, month={Jun}, pages={306–317} }
 @book{hester_cakir_nelson_khorram_2006, title={Integration of high resolution imagery in cost-effective assessment of land use practices influencing erosion and sediment yield}, volume={221}, journal={Water Resource Research Institute final report (Center for Earth Observation Technical Report)}, author={Hester, D. B. and Cakir, H. I. and Nelson, S. A. C. and Khorram, S.}, year={2006} }
 @article{knight_lunetta_ediriwickrema_khorrarn_2006, title={Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology-based approach}, volume={43}, ISSN={["1943-7226"]}, DOI={10.2747/1548-1603.43.1.1}, abstractNote={Currently available land cover data sets for large geographic regions are produced on an intermittent basis and are often dated. Ideally, annually updated data would be available to support environmental status and trends assessments and ecosystem process modeling. This research examined the potential for vegetation phenology-based land cover classification over the 52,000 km2 Albemarle-Pamlico estuarine system (APES) that could be performed annually. Traditional hyperspectral image classification techniques were applied using MODIS-NDVI 250 m 16-day composite data over calendar year 2001 to support the multi-temporal image analysis approach. A reference database was developed using archival aerial photography that provided detailed mixed pixel cover-type data for 31,322 sampling sites corresponding to MODIS 250 m pixels. Accuracy estimates for the classification indicated that the overall accuracy of the classification ranged from 73% for very heterogeneous pixels to 89% when only homogeneous pixels were examined. These accuracies are comparable to similar classifications using much higher spatial resolution data, which indicates that there is significant value added to relatively coarse resolution data though the addition of multi-temporal observations.}, number={1}, journal={GISCIENCE & REMOTE SENSING}, author={Knight, Joseph F. and Lunetta, Ross S. and Ediriwickrema, Jayantha and Khorrarn, Siamak}, year={2006}, pages={1–23} }
 @book{khorram_nelson_cakir_hester_2005, title={Integration of high resolution imagery in cost-effective assessment of land use practices influencing erosion and sediment yield}, volume={221}, journal={Water Resource Research Institute final report (Center for Earth Observation Technical Report)}, author={Khorram, S. and Nelson, S. A. C. and Cakir, H. and Hester, D. B.}, year={2005} }
 @article{morisette_khorram_2000, title={Accuracy assessment curves for satellite-based change detection}, volume={66}, number={7}, journal={Photogrammetric Engineering and Remote Sensing}, author={Morisette, J. T. and Khorram, S.}, year={2000}, pages={875–880} }
 @article{dai_khorram_1999, title={A feature-based image registration algorithm using improved chain-code representation combined with invariant moments}, volume={37}, ISSN={["0196-2892"]}, DOI={10.1109/36.789634}, abstractNote={A new feature-based approach to automated image-to-image registration is presented. The characteristic of this approach is that it combines an invariant-moment shape descriptor with improved chain-code matching to establish correspondences between the potentially matched regions detected from the two images. It is robust in that it overcomes the difficulties of control-point correspondence by matching the images both in the feature space, using the principle of minimum distance classifier (based on the combined criteria), and sequentially in the image space, using the rule of root mean-square error (RMSE). In image segmentation, the performance of the Laplacian of Gaussian operators is improved by introducing a new algorithm called thin and robust zero crossing. After the detected edge points are refined and sorted, regions are defined. Region correspondences are then performed by an image-matching algorithm developed in this research. The centers of gravity are then extracted from the matched regions and are used as control points. Transformation parameters are estimated based on the final matched control-point pairs. The algorithm proposed is automated, robust, and of significant value in an operational context. Experimental results using multitemporal Landsat TM imagery are presented.}, number={5}, journal={IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}, author={Dai, XL and Khorram, S}, year={1999}, month={Sep}, pages={2351–2362} }
 @article{dai_khorram_1999, title={Data fusion using artificial neural networks: A case study on multitemporal image analysis}, volume={23}, DOI={10.1016/s0198-9715(98)00051-9}, abstractNote={In this paper, we present a formulation framework for data fusion in land cover characterization and a case study on multitemporal change analysis using artificial neural networks. Neural networks have the coherent advantage of overcoming the difficulties in merging data from multiple sources since they are distribution-free and it is not required to model the data. Based on a review on remotely sensed data fusion, the neural network-based approach to multitemporal change analysis and its implementation are then explored, which includes network training issues and algorithms, such as the backpropagation algorithm, selection of network architecture including number of hidden layers and number of nodes in each layer, and parameter determination. Experimental results using multitemporal Thematic Mapper (TM) data are provided. Several factors contribute to the selection of an appropriate fusion technique and the neural network-based approach is found to be one of the promising methods. ©}, number={2}, journal={Computers, Environment and Urban Systems}, author={Dai, X. L. and Khorram, S.}, year={1999}, pages={19–31} }
 @article{morisette_khorram_mace_1999, title={Land-cover change detection enhanced with generalized linear models}, volume={20}, ISSN={["0143-1161"]}, DOI={10.1080/014311699211750}, abstractNote={This paper explores the use of generalized linear models (GLMs) for enhancing standard methods of satellite-based land-cover change detection. It starts by generalizing satellite-based change-detection algorithms in a modelling context and then gives an overview of GLMs. It goes onto describe how GLMs can fit into the context of existing change-detection methods. By way of example, using a change detection over two locations in North Carolina, USA, using Landsat Thematic Mapper data, it shows how the models provide a quantitative approach to image-based change detection. The application of GLMs requires special consideration of the spatial correlation of geographical data and how this effects the use of GLMs. The paper describes the use of preliminary variogram analysis on the image data for initial sampling considerations. For the binary response (change/no-change) derived from the reference data, a 'joint-count' test is used to assess their independence. Finally, the model error term is checked through ...}, number={14}, journal={INTERNATIONAL JOURNAL OF REMOTE SENSING}, author={Morisette, JT and Khorram, S and Mace, T}, year={1999}, month={Sep}, pages={2703–2721} }
 @article{dai_khorram_1999, title={Remotely sensed change detection based on artificial neural networks}, volume={65}, number={3}, journal={Photogrammetric Engineering and Remote Sensing}, author={Dai, X. L. and Khorram, S.}, year={1999}, pages={1187–1194} }
 @article{dai_karimi_khorram_khattak_hummer_1999, title={Roadway feature extraction and delineation fron high-resolution satellite imagery}, number={1999 May}, journal={EOM}, author={Dai, X. L. and Karimi, H. A. and Khorram, S. and Khattak, A. J. and Hummer, J. E.}, year={1999}, pages={34–37} }
 @article{karimi_dai_khattak_khorram_hummer_1999, title={Techniques for automated extraction of roadway inventory features from high-resolution satellite imagery}, volume={14}, DOI={10.1080/10106049908542099}, abstractNote={Abstract The emergence of high‐resolution satellite imagery is attracting new applications which can take advantage of remotely sensed data for mapping, inventory, and change detection. Automated collection of roadway inventory features is one such application. To this end, it is important to investigate the performance of conventional feature extraction techniques when applied to high‐resolution images and to develop new techniques for extraction of roadway features using one‐meter, or higher, resolution imagery. In this paper, classification‐ based and edge detection‐based techniques potential for automated extraction of roadway features from high‐resolution satellite imagery are described, tested, and evaluated. Of possible techniques, the applicability of conventional classification algorithms, the Thin and Robust Zero‐Crossing edge detector based on the Laplacian of Gaussian operator, and seeded region growing segmentation is investigated. The advantages and disadvantages of each technique for extrac...}, number={2}, journal={Geocarto International}, author={Karimi, H. A. and Dai, X. L. and Khattak, A. J. and Khorram, S. and Hummer, J. E.}, year={1999}, pages={5–16} }
 @article{dai_khorram_1998, title={A hierarchical methodology framework for multisource data fusion in vegetation classification}, volume={19}, ISSN={["1366-5901"]}, DOI={10.1080/014311698213911}, abstractNote={This Letter presents a new methodological framework for a hierarchical data fusion system for vegetation classification using multi-sensor and multitemporal remotely sensed imagery. The uniqueness of the approach is that the overall structure of the fusion system is built upon a hierarchy of vegetation canopy attributes that can be remotely detected by sensors. The framework consists of two key components: an automated multisource image registration system and a hierarchical model for multi-sensor and multi-temporal data fusion.}, number={18}, journal={INTERNATIONAL JOURNAL OF REMOTE SENSING}, author={Dai, X and Khorram, S}, year={1998}, month={Dec}, pages={3697–3701} }
 @article{morisette_khorram_1998, title={Exact binomial confidence interval for proportions}, volume={64}, number={4}, journal={Photogrammetric Engineering and Remote Sensing}, author={Morisette, J. T. and Khorram, S.}, year={1998}, pages={281–283} }
 @article{dai_khorram_1998, title={The effects of image misregistration on the accuracy of remotely sensed change detection}, volume={36}, ISSN={["0196-2892"]}, DOI={10.1109/36.718860}, abstractNote={Image misregistration has become one of the significant bottlenecks for improving the accuracy of multisource data analysis, such as data fusion and change detection. In this paper, the effects of misregistration on the accuracy of remotely sensed change detection were systematically investigated and quantitatively evaluated. This simulation research focused on two interconnected components. In the first component, the statistical properties of the multispectral difference images were evaluated using semivariograms when multitemporal images were progressively misregistered against themselves and each other to investigate the band, temporal, and spatial frequency sensitivities of change detection to image misregistration. In the second component, the ellipsoidal change detection technique, based on the Mahalanobis distance of multispectral difference images, was proposed and used to progressively detect the land cover transitions at each misregistration stage for each pair of multitemporal images. The impact of misregistration on change detection was then evaluated in terms of the accuracy of change detection using the output from the ellipsoidal change detector. The experimental results using Landsat Thematic Mapper (TM) imagery are presented. It is interesting to notice that, among the seven TM bands, band 4 (near-infrared channel) is the most sensitive to misregistration when change detection is concerned. The results from false change analysis indicate a substantial degradation in the accuracy of remotely sensed change detection due to misregistration. It is shown that a registration accuracy of less than one-fifth of a pixel is required to achieve a change detection error of less than 10%.}, number={5}, journal={IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}, author={Dai, XL and Khorram, S}, year={1998}, month={Sep}, pages={1566–1577} }
 @article{ediriwickrema_khorram_1997, title={Hierarchical maximum-likelihood classification for improved accuracies}, volume={35}, ISSN={["0196-2892"]}, DOI={10.1109/36.602523}, abstractNote={Among the supervised parametric classification methods, the maximum-likelihood (MLH) classifier has become popular and widespread in remote sensing. Reliable prior probabilities are not always freely available, and it is a common practice to perform the MLH classification with equal prior probabilities. When equal prior probabilities are used, the advantages in MLH classification may not be attained. 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. The TM pixels were visualized in multidimensional feature space relative to the spectral class probability surfaces. The pixels that fell within more than one probability region or outside all probability regions were categorized as the pixels likely to misclassify. Prior probabilities were estimated from the pixels that fell within spectral class probability regions. The pixels most likely to be correctly classified do not need extra information and were classified according to the probability region in which they fell. The pixels likely to be misclassified need additional information and were classified by MLH classification with the estimated prior probabilities. The classified image resulting from the HPC showed increased accuracy over three classification methods. Visualization of pixels in multidimensional feature space, relative to the spectral class probability reforms, overcome the practical difficulty in estimating prior probabilities while utilizing the available information.}, number={4}, journal={IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}, author={Ediriwickrema, J and Khorram, S}, year={1997}, month={Jul}, pages={810–816} }
 @article{brockhaus_campbell_khorram_bruck_stallings_1991, title={Forest decline model development with LANDSAT-TM, SPOT, DEM DATA}, volume={29}, journal={IEEE Transactions on Geoscience and Remote Sensing}, author={Brockhaus, J. A. and Campbell, M. V. and Khorram, S. and Bruck, R. I. and Stallings, C.}, year={1991}, pages={459–466} }
 @article{khorram_brockhaus_bruck_campbell_1990, title={MODELING AND MULTITEMPORAL EVALUATION OF FOREST DECLINE WITH LANDSAT TM DIGITAL DATA}, volume={28}, ISSN={["0196-2892"]}, DOI={10.1109/tgrs.1990.573008}, number={4}, journal={IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}, author={KHORRAM, S and BROCKHAUS, JA and BRUCK, RI and CAMPBELL, MV}, year={1990}, month={Jul}, pages={746–748} }
 @article{brockhaus_campbell_bruck_khorram_1989, title={Analysis of forest decline in the Southern Appalachian Mountains.}, volume={41}, journal={Proceedings of the American Society of Photogrammetry and Remote Sensing}, author={Brockhaus, J. A. and Campbell, M. V. and Bruck, R. I. and Khorram, S.}, year={1989}, pages={419–429} }
 @article{khorram_brockhaus_bruck_campbell_1989, title={Multi-temporal modeling of forest decline from Landsat TM digital data}, volume={37}, journal={Proceedings of the International Society of Remote Sensing}, author={Khorram, S. and Brockhaus, J. A. and Bruck, R. I. and Campbell, M. V.}, year={1989}, pages={771–779} }
 @inproceedings{bruck_bradow_brockhaus_cure_khorram_mcdaniel_modena_robarge_smithson_1989, title={Observations of forest decline in the boreal montane ecosystems of Mt. Mitchell, N.C.}, booktitle={Proceedings of the U.S.-F.R.G. Symposium on Forest Decline, Burlington, VT, Oct. 19-24, 1987 (USDA Forest Service Technical publication #120)}, publisher={USDA Forest Service}, author={Bruck, R. I. and Bradow, R. and Brockhaus, J. and Cure, B. and Khorram, S. and McDaniel, A. and Modena, S. and Robarge, W. and Smithson, P.}, year={1989}, pages={97–107} }
 @article{campbell_brockhaus_bruck_khorram_1989, title={The effect of field plot location errors within TM data on forest decline model development}, volume={41}, journal={Proceedings of the American Society of Photogrammetry and Remote Sensing}, author={Campbell, M. V. and Brockhaus, J. A. and Bruck, R. I. and Khorram, S.}, year={1989}, pages={430–437} }
 @book{khorram_brockhaus_cheshire_1988, title={Comparison of Landsat MSS and TM data for urban land use classification}, number={65}, journal={Comparison of Landsat MSS and TM data for urban land use classification}, publisher={Raleigh, N.C.: NCSU School of Forest Resources}, author={Khorram, S. and Brockhaus, J. A. and Cheshire, H. M.}, year={1988}, pages={23} }