Combining per-pixel and object-based classifications for mapping land cover over large areas
Combining per-pixel and object-based classifications for mapping land cover over large areas
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DOI：
10.1080/01431161.2013.873151
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发表时间：
2014-01
影响因子：
3.4
通讯作者：
H. Costa;H. Carrão;F. Bação;M. Caetano
中科院分区：
文献类型：
--
作者：
H. Costa;H. Carrão;F. Bação;M. Caetano
大量的国家和区域应用需要覆盖大面积的土地覆盖信息。基于目视解译的人工分类和数字逐像元分类是利用遥感图像进行大面积土地覆盖制图最常用的两种方法，但这两种方法都存在一些缺点。本文测试了一种在中等空间分辨率图像下导出不受空间分辨率约束的具有预定义最小映射单元（MMU）的产品的方法。该方法由传统的有监督的逐像素分类和后分类处理组成，后分类处理包括图像分割和语义图泛化。该方法使用在葡萄牙一个地区收集的AWiFS数据进行了测试，绘制了15个土地覆盖等级，MMU为10公顷。在95%置信水平下，该地图的主题精度为72.6±3.7%，比逐像素分类精度高出约10%。结果表明，中空间分辨率图像分割和语义地图概化可以在操作环境中自动生成具有预定义MMU的大面积土地覆盖地图。
A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.