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钟燕飞
博士 教授 博士生导师
武汉大学 测绘遥感信息工程国家重点实验室
主要从事智能化遥感数据提取分析与应用方向的研究,包括:高光谱遥感信息处理、高分辨率遥感影像理解、多源遥感数据地学解译等。
个性化签名
- 姓名:钟燕飞
- 目前身份:在职研究人员
- 担任导师情况:博士生导师
- 学位:博士
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学术头衔:
- 职称:高级-教授
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学科领域:
摄影测量与遥感技术
- 研究兴趣:主要从事智能化遥感数据提取分析与应用方向的研究,包括:高光谱遥感信息处理、高分辨率遥感影像理解、多源遥感数据地学解译等。
钟燕飞,武汉大学测绘遥感信息工程国家重点实验室教授,博士生导师,遥感影像信息处理研究室主任,RSIDEA研究组负责人。主要从事智能化遥感数据提取分析与应用方向的研究,包括:高光谱遥感信息处理、高分辨率遥感影像理解、多源遥感数据地学解译等。发表SCI论文60余篇,其中一、二区论文51篇,被引用1700余次,2篇论文入选ESI高被引/热点论文;申请国家发明专利6项。
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成果数
3
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2016,116():73-85
2016年05月18日
Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
Scene classification, Bag-of-features, Feature coding, Spectral information, Structural information, Very high spatial resolution, Land-use classification, Remote sensing
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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2016,119():49-63
2016年10月26日
Recently, many blind source separation (BSS)-based techniques have been applied to hyperspectral unmixing. In this paper, a new blind spectral unmixing method based on sparse component analysis (BSUSCA) is proposed to solve the problem of highly mixed data. The BSUSCA algorithm consists of an alternative scheme based on two-block alternating optimization, by which we can simultaneously obtain the endmember signatures and their corresponding fi.actional abundances. According to the spatial distribution of the endmembers, the sparse properties of the fractional abundances are considered in the proposed algorithm. A sparse component analysis (SCA)-based mixing matrix estimation method is applied to update the endmember signatures, and the abundance estimation problem is solved by the alternating direction method of multipliers (ADMM). SCA is utilized for the unmixing due to its various advantages, including the unique solution and robust modeling assumption. The robustness of the proposed algorithm is verified through simulated experimental study. The experimental results using both simulated data and real hyperspectral remote sensing images confirm the high efficiency and precision of the proposed algorithm. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
Hyperspectral remote sensing, Hyperspectral unmixing, Blind source separation, Sparse component analysis
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2016,54(4):2108-2123
2016年04月20日
Due to the complex arrangements of the ground objects in high spatial resolution (HSR) imagery scenes, HSR imagery scene classification is a challenging task, which is aimed at bridging the semantic gap between the low-level features and the high-level semantic concepts. A combination of multiple complementary features for HSR imagery scene classification is considered a potential way to improve the performance. However, the different types of features have different characteristics, and how to fuse the different types of features is a classic problem. In this paper, a Dirichlet-derived multiple topic model (DMTM) is proposed to fuse heterogeneous features at a topic level for HSR imagery scene classification. An efficient algorithm based on a variational expectation maximization framework is developed to infer the DMTM and estimate the parameters of the DMTM. The proposed DMTM scene classification method is able to incorporate different types of features with different characteristics, no matter whether these features are local or global, discrete or continuous. Meanwhile, the proposed DMTM can also reduce the dimension of the features representing the HSR images. In our experiments, three types of heterogeneous features, i.e., the local spectral feature, the local structural feature, and the global textural feature, were employed. The experimental results with three different HSR imagery data sets show that the three types of features are complementary. In addition, the proposed DMTM is able to reduce the dimension of the features representing the HSR images, to fuse the different types of features efficiently, and to improve the performance of the scene classification over that of other scene classification algorithms based on spatial pyramid matching, probabilistic latent semantic analysis, and latent Dirichlet allocation.
Terms High spatial resolution (HSR), latent Dirichlet allocation (LDA), multiple features, probabilistic latent semantic analysis (PLSA), remote sensing, scene classification
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