针对机载激光雷达(light detection and ranging,LiDAR)数据与航空彩色影像的数据特点,提出了一种面向对象的多源数据融合分类方法。该方法根据影像光谱特性将航空影像分割为若干个同质区域,通过综合考察每个区域内LiDAR数据的滤波结果...针对机载激光雷达(light detection and ranging,LiDAR)数据与航空彩色影像的数据特点,提出了一种面向对象的多源数据融合分类方法。该方法根据影像光谱特性将航空影像分割为若干个同质区域,通过综合考察每个区域内LiDAR数据的滤波结果、空间离散度、高差值和航空影像光谱信息,判断各区域归属为哪一类。实验表明,该方法能够有效地分离房屋、树木和裸露地3种基本地物。展开更多
The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Be...The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.展开更多
文摘针对机载激光雷达(light detection and ranging,LiDAR)数据与航空彩色影像的数据特点,提出了一种面向对象的多源数据融合分类方法。该方法根据影像光谱特性将航空影像分割为若干个同质区域,通过综合考察每个区域内LiDAR数据的滤波结果、空间离散度、高差值和航空影像光谱信息,判断各区域归属为哪一类。实验表明,该方法能够有效地分离房屋、树木和裸露地3种基本地物。
基金financially supported by the Opening Foundation of the Key Laboratory of Agricultural Information Technology,Ministry of Agriculture,China (2014009)the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)+1 种基金the National Natural Science Fo undation of China (41271112)the Youth Foundation of Heilongjiang Academy of Agricultural Science,China (QN024)
文摘The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.
