Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images
文献类型：期刊论文
| 作者 | Zhang, Xia1; Sun, Yanli1; Shang, Kun1; Zhang, Lifu1; Wang, Shudong1 |
| 刊名 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 9期号:9页码:4117-4128 |
| 关键词 | HEIHE RIVER-BASIN HYPERSPECTRAL DATA REFLECTANCE VEGETATION PRODUCTS MOSAICS BRDF SURFACE AVIRIS CHINA |
| 通讯作者 | Sun, Yanli (sunyanliok@163.com) |
| 英文摘要 | Remote sensing plays a significant role for crop classification. Accurate crop classification is a common requirement to precision agriculture, including crop area estimation, crop yield estimation, precision crop management, etc. This paper developed a new crop classification method involving the construction and optimization of the vegetation feature band set (FBS) and combination of FBS and object-oriented classification (OOC) approach. In addition to the spectral and textural features of the original image, 20 spectral indices sensitive to the vegetation's biological parameters are added to the FBS to distinguish specific vegetation. A spectral dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed to improve the separability between class pairs while reducing data redundancy. OOC approach is conducted on the optimized FBS based on CPS to reduce the salt-and-pepper noise. The proposed classification method was validated by two airborne hyperspectral images. The first image acquired in an agricultural area of Japan includes seven crop types, and the second image acquired in a rice breeding area consists of six varieties of rice. For the first image, the proposed method distinguished different vegetation with an overall accuracy of 97.84% and kappa coefficient of 0.96. For the second image, the proposed method distinguished the rice varieties accurately, achieving the highest overall accuracy (98.65%) and kappa coefficient (0.98). Results demonstrate that the proposed method can significantly improve crop classification accuracy and reduce edge effects, and that textural features combined with spectral indices sensitive to the chlorophyll, carotenoid, and Anthocyanin indicators contribute significantly to crop classification. Therefore, it is an effective approach for classifying crop species, monitoring invasive species, as well as precision agriculture related applications. © 2016 IEEE. |
| 学科主题 | Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162702564626 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39281]  |
| 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. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100101, China 3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 4.100101, China 5. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 6.100101, China 7. Laboratory of Hyperspectral Remote Sensing, Institute of Remote Sensing and Digital Earth, Beijing 8.100101, China |
| 推荐引用方式 GB/T 7714 | Zhang, Xia,Sun, Yanli,Shang, Kun,et al. Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(9):4117-4128. |
| APA | Zhang, Xia,Sun, Yanli,Shang, Kun,Zhang, Lifu,&Wang, Shudong.(2016).Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(9),4117-4128. |
| MLA | Zhang, Xia,et al."Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9.9(2016):4117-4128. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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