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首页> 外文会议>Earth Observation and Remote Sensing Applications (EORSA), 2012 Second International Workshop on >Object-oriented land cover classification using multi-temporal HJ-1 CCD imagery: A case study in central Shandong province, China
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Object-oriented land cover classification using multi-temporal HJ-1 CCD imagery: A case study in central Shandong province, China

机译：基于多时相HJ-1 CCD图像的面向对象土地覆盖分类-以中国山东省中部为例

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摘要

This paper focuses on object-oriented land cover classification using multi-temporal remotely sensed imagery. We proposed an approach by building rules using multi-temporal HJ-1 CCD imagery and other auxiliary data to classify various land cover types in central Shandong province. We analyzed the seasonal dynamics of vegetation indices (EVI (Enhanced Vegetation index) and NDVI). Vegetation index time series of multi-temporal images can help differentiate forest types. Given the difficulties of vegetation classification, especially in mountainous area, more information available such as DEM, slope, spatial features and priori knowledge were also utilized. The overall accuracy and Kappa coefficient of land cover classification are 80.1% and 0.76, respectively. The results show that besides the spectral information, texture, DEM, slope and auxiliary data are very useful for land cover classification. Multi-temporal information can improve the vegetation classification result significantly and meanwhile has much potential to be explored.
机译：本文着重于利用多时相遥感影像进行面向对象的土地覆被分类。我们提出了一种利用多时相HJ-1 CCD影像和其他辅助数据建立规则的方法，以对山东省中部的各种土地覆盖类型进行分类。我们分析了植被指数（EVI（增强植被指数）和NDVI）的季节性动态。多时相影像的植被指数时间序列可以帮助区分森林类型。考虑到植被分类的困难，尤其是在山区，还利用了更多可用信息，例如DEM，坡度，空间特征和先验知识。土地覆被分类的总体准确性和卡帕系数分别为80.1％和0.76。结果表明，除光谱信息外，纹理，DEM，坡度和辅助数据对于土地覆被分类非常有用。多时相信息可以显着改善植被分类结果，同时具有很大的开发潜力。

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