@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{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{khattak_karimi_dai_hummer_1998, title={High-resolution satellite imagery aids roadway data collection}, number={1998 Nov.}, journal={Public Works}, author={Khattak, A. J. and Karimi, H. A. and Dai, X. L. and Hummer, J. E.}, year={1998}, pages={28–30} }
 @article{dai_snyder_bilbro_williams_cowan_1998, title={Left-ventricle boundary detection from nuclear medicine images}, volume={11}, ISSN={["0897-1889"]}, DOI={10.1007/BF03168721}, abstractNote={We present here a new algorithm for segmentation of nuclear medicine images to detect the left-ventricle (LV) boundary. In this article, other image segmentation techniques, such as edge detection and region growing, are also compared and evaluated. In the edge detection approach, we explored the relationship between the LV boundary characteristics in nuclear medicine images and their radial orientations: we observed that no single brightness function (eg, maximum of first or second derivative) is sufficient to identify the boundary in every direction. In the region growing approach, several criteria, including intensity change, gradient magnitude change, gradient direction change, and running mean differences, were tested. We found that none of these criteria alone was sufficient to successfully detect the LV boundary. Then we proposed a simple but successful region growing method—Contour-Modified Region Growing (CMRG). CMRG is an easy-to-use, robust, and rapid image segmentation procedure. Based on our experiments, this method seems to perform quite well in comparison to other automated methods that we have tested because of its ability to handle the problems of both low signal-to-noise ratios (SNR) as well as low image contrast without any assumptions about the shape of the left ventricle.}, number={1}, journal={JOURNAL OF DIGITAL IMAGING}, author={Dai, XL and Snyder, WE and Bilbro, GL and Williams, R and Cowan, R}, year={1998}, month={Feb}, pages={10–20} }
 @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{karimi_dai_khatak_hummer_1998, title={The emergence of high-resolution satellite digital imagery for acquisition roadway inventory features}, volume={3}, number={1}, journal={Space Energy and Transportation}, author={Karimi, H. A. and Dai, X. L. and Khatak, A. J. and Hummer, J. E.}, year={1998}, pages={19–26} }