Land use/cover classification in the Brazilian Amazon using satellite images.
Land use/cover classification in the Brazilian Amazon using satellite images.
复制标题
使用卫星图像在巴西亚马逊中的土地使用/覆盖分类。
DOI：
10.1590/s0100-204x2012000900004
复制
发表时间：
2012-09
影响因子：
0.8
通讯作者：
Sant'anna SJ
中科院分区：
文献类型：
--
作者：
Lu D;Batistella M;Li G;Moran E;Hetrick S;Freitas CD;Dutra LV;Sant'anna SJ
土地利用/覆盖分类是遥感技术的重要应用之一。然而，由于复杂的生物物理环境和遥感数据本身的局限性，绘制准确的土地利用/覆盖空间分布图是一项挑战，特别是在潮湿的热带地区。本文回顾了有关的实验，土地利用/覆盖分类在巴西亚马逊十年。通过对分类结果的综合分析，认为遥感数据所蕴含的空间信息对土地利用/覆盖分类的改进起着至关重要的作用。将适当的纹理图像纳入多光谱波段和使用基于分割的方法是改善土地利用/覆盖分类的宝贵途径，特别是对于高空间分辨率图像。光学传感器数据中的多分辨率图像的数据融合对于视觉解释是至关重要的，但可能不会提高分类性能。相比之下，当使用适当的数据融合方法时，光学和雷达数据的集成确实提高了分类性能。在可用的分类算法中，最大似然分类器仍然是提供相当好的准确性的重要方法，但是非参数算法，例如分类树分析，具有提供更好结果的潜力。然而，它们通常需要更多的时间来实现参数优化。适当使用基于层次的方法是主要根据历史遥感数据进行准确的土地利用/覆盖分类的基础。
Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.