Fusion of airborne discrete-return LiDAR and hyperspectral data for land cover classification
文献类型：期刊论文
| 作者 | Luo, Shezhou1; Wang, Cheng1; Xi, Xiaohuan1; Zeng, Hongcheng1; Li, Dong1; Xia, Shaobo1; Wang, Pinghua1 |
| 刊名 | Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 8期号:1 |
| 关键词 | PEARL RIVER DELTA LAND EXPANSION SPATIOTEMPORAL PATTERNS METROPOLITAN-AREA DRIVING FORCES GROWTH DYNAMICS SPRAWL REGION CONSTRUCTION |
| 通讯作者 | Wang, Cheng (wangcheng@radi.ac.cn) |
| 英文摘要 | Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions. © 2015 by the authors; licensee MDPI, Basel, Switzerland. |
| 学科主题 | Remote Sensing |
| 类目[WOS] | Remote Sensing |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20160701945645 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39227]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. Department of Geography and Program in Planning, University of Toronto, 100St. George St., Toronto 3.ON, Canada 4. Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto 5.ON, Canada |
| 推荐引用方式 GB/T 7714 | Luo, Shezhou,Wang, Cheng,Xi, Xiaohuan,et al. Fusion of airborne discrete-return LiDAR and hyperspectral data for land cover classification[J]. Remote Sensing,2016,8(1). |
| APA | Luo, Shezhou.,Wang, Cheng.,Xi, Xiaohuan.,Zeng, Hongcheng.,Li, Dong.,...&Wang, Pinghua.(2016).Fusion of airborne discrete-return LiDAR and hyperspectral data for land cover classification.Remote Sensing,8(1). |
| MLA | Luo, Shezhou,et al."Fusion of airborne discrete-return LiDAR and hyperspectral data for land cover classification".Remote Sensing 8.1(2016). |
入库方式： OAI收割
来源：遥感与数字地球研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。