黄作维,胡光伟,谢世雄.基于光谱解混的城市地物分类研究[J].农业机械学报,2018,49(10):205-211.
HUANG Zuowei,HU Guangwei,XIE Shixiong.Investigation on Urban Object Classification Based on Spectral Unmixing[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):205-211.
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基于光谱解混的城市地物分类研究   [下载全文]
Investigation on Urban Object Classification Based on Spectral Unmixing   [Download Pdf][in English]
投稿时间:2017-11-30  
DOI:10.6041/j.issn.1000-1298.2018.10.023
中文关键词:  地物分类  光谱解混  端元提取  光谱空间特征
基金项目:湖南省自然科学基金项目(2017JJ2072、2017JJ3056)
作者单位
黄作维 湖南工业大学 
胡光伟 湖南工业大学 
谢世雄 湖南工业大学 
中文摘要:高光谱遥感信息提取面临的突出问题是混合像元的广泛存在,如何有效地解译混合像元是高光谱遥感应用的关键问题。混合像元不仅影响地物的识别和分类精度,而且是遥感技术向定量化发展的重要障碍,混合像元分解是解决混合像元问题最有效的方法,能够克服高光谱图像空间分辨率的限制。针对传统混合像元分解算法的缺点,基于优化的候选端元判断方法及端元提取的并行设计方法,提出了一种优化的混合像元分解方法,实现了光谱特征信息和空间特征信息的有机融合。通过模拟高光谱数据和真实遥感图像进行仿真研究,实验结果表明,该方法能得到精确的端元和对应的丰度,获得较好的解混效果,为城市地物分类提供了有力支持。
HUANG Zuowei  HU Guangwei  XIE Shixiong
Hunan University of Technology,Hunan University of Technology and Hunan University of Technology
Key Words:object-classification  spectral unmixing  endmember extraction  spectral spatial characteristic
Abstract:One of the prominent problems in hyperspectral remote sensing is the existing of mixed pixel widely. How to effectively interpret mixed pixels is an important problem of hyperspectral remote sensing applications. It is not only a problem of mixed pixels effects identification and classification precision of objects, but also a major barrier for the development of remote sensing technology. Mixed pixel decomposition, which is the most effective method to solve the mixed pixel problem, can break through the limitation of spatial resolution. Aiming to the shortcoming of the traditional algorithm of mixed pixel decomposition, an improved method of mixed pixels was put forward, which can take account of the spatial correlation of spectral information and spectral information, and multi-core parallel processing method to raise its efficiency. The endmembers were automatically extracted, and the abundance charts corresponding to each endmember were obtained at the same time. The performance of the proposed algorithm was verified by using actual hyperspectral image. The experimental results on simulated and real hyperspectral image demonstrated that the proposed algorithm can overcome the shortcomings of traditional method and obtain more accurate endmembers and corresponding abundance, which can provide a strong support for urban object classification.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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