VHR IMAGE CHANGE DETECTION BASED ON DISCRIMINATIVE DICTIONARY LEARNING

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	VHR IMAGE CHANGE DETECTION BASED ON DISCRIMINATIVE DICTIONARY LEARNING
	Ding, Kun; Huo, Chunlei; Xu, Yuan; Pan, Chunhong
	2013
会议名称	2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
会议录名称	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
页码	3512-3516
会议日期	2013
会议地点	Vancouver
摘要	The difficulty of Very High Resolution (VHR) image change detection is mainly due to the low separability between the changed and unchanged class. The traditional approaches usually address the problem by solving the feature extraction and classification separately, which cannot ensure that the classification algorithm makes the best use of the features. Considering this, we propose a novel approach that combines the feature extraction and the classification task by utilizing the sparse representation algorithm with discriminative dictionary. Experiments on real data sets show that our method achieves effective results.
关键词	Change Detection Vhr Remote Sensing Image Sparse Representation Discriminative Dictionary
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/4731
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
通讯作者	Ding, Kun
作者单位	National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Ding, Kun,Huo, Chunlei,Xu, Yuan,et al. VHR IMAGE CHANGE DETECTION BASED ON DISCRIMINATIVE DICTIONARY LEARNING[C],2013:3512-3516.

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06638311.pdf（801KB）	会议论文		开放获取	CC BY-NC-SA	浏览