摘要
中国南方丘陵区地形破碎,地物分布复杂,丘陵区水田的光谱特征相对于平原区较混杂,传统的基于像元的遥感数据获取受异质性因素的影响,无法利用单一时(季)像及特定的图像自动识别规则提取精度较高的水田分布信息。针对这一问题,该文基于多时像HJ-1A/1B卫星图像,结合地面调查,以湖南省湘潭市为研究区,在易康(e Cognition)软件平台上分别以光谱特征为主要参考的多层最邻近分类法和以在特征知识库支持下的决策树分类法对丘陵区水田进行图像识别。结果表明:分层最邻近分类法比单一最邻近分类提取的精度高,但在相同分割尺度下,利用特征知识库支持下的决策树分类提取水田的精度达到最高,为90.25%,总Kappa系数为0.79,说明特征知识库支持下的决策树分类方法比最邻近分类法更加适合丘陵区水田的遥感识别。
Identification of paddy fields in the hilly regions is important for policy making of food self-sufficiency in China. However, extracting image information using current image analysis techniques is difficult because of the unique terrain of hilly regions. The traditional pixel-based analysis of remotely sensed data is usually affected by pixel heterogeneity, mixed pixels, and spectral similarity, thus leading to the inaccurate identification of paddy fields in hilly regions. This study aimed to find other methods for accurate paddy field identification in hilly regions. The study area was Xiangtan City located in the mid-east of Hunan province, a good representative of hilly regions. In Xiangtan city, the land use change markedly increases with rapid economic development, leading to gradual decline of cultivated land. The Chinese environment and disaster mitigation satellite （i.e., HJ-1A/1B） image of the region was data source for land use map. The HJ-1A star was equipped with a charge-coupled device （CCD） camera and a hyperspectral imager, whereas the HJ-1B star was equipped with CCD and infrared （IR） cameras. The satellite observes the ground in widths of 700 km with a ground pixel resolution of 30 m by four multispectral imaging steps. The object-oriented image analysis technique is a new type of automatic technique under a computer environment. The information carrier used was multi-scale objects composed of multiple adjacent pixels containing rich semantic information. Image segmentation is an important classification step because high-precision remote sensing （RS） image classification depends on good segmentation. The multi-scale image segmentation algorithm was applied in the preliminary object extraction to fully interpret the RS images with the different spectral features, shape, and textural features of real ground targets. The configuration of multi-scale segmentation thresholds directly affected the integrity of features extracted from RS images. In this study, the cultivated and uncu
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2015年第11期186-193,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重大专项-中国科学院战略性先导科技专项(XDA0505107)
关键词
遥感
决策树
图像识别
面向对象
耕地
水田
丘陵区
最邻近分类
remote sensing
decision tree classifiers
image recognization
object-oriented
cultivated land
paddy field
hilly region
k-nearest neighbor
