Xiaolong Dai
North Carolina State University
17 Papers
142 Citations
Xiaolong Dai is an academic researcher from North Carolina State University. The author has contributed to research in topics: Change detection & Image segmentation. The author has an hindex of 9, co-authored 17 publications.
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Papers
The effects of image misregistration on the accuracy of remotely sensed change detection
Xiaolong Dai,Siamak Khorram +1 more
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%.
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Development of a feature-based approach to automated image registration for multitemporal and multisensor remotely sensed imagery
Xiaolong Dai,S. Khorram +1 more
- 03 Aug 1997
TL;DR: A new feature-based approach to automated multitemporal and multisensor image registration is presented that combines moment invariant shape descriptors with modified chain code correlation to establish the correspondences between potential matched regions in two images.
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A hierarchical methodology framework for multisource data fusion in vegetation classification
Xiaolong Dai,Siamak Khorram +1 more
TL;DR: In this article, a hierarchical data fusion system for vegetation classification using multi-sensor and multitemporal remotely sensed imagery is presented, where the overall structure of the fusion system is built upon a hierarchy of vegetation canopy attributes that can be remotely detected by sensors.
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Quantification of the impact of misregistration on the accuracy of remotely sensed change detection
Xiaolong Dai,S. Khorram +1 more
- 03 Aug 1997
TL;DR: The impact of misreg registration on the accuracy of change detection is quantitatively investigated using TM imagery and the ellipsoidal change detection technique is proposed and used to progressively detect the land cover transitions at each misregistration stage.
21
Development of a new automated land cover change detection system from remotely sensed imagery based on artificial neural networks
Xiaolong Dai,S. Khorram +1 more
- 03 Aug 1997
TL;DR: Based on their experiments, it has been proven that this technique is successful and has immense implications on land cover change detection and quantification at all levels of applications ranging from local to global in scale.
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