基于SAR提高喀斯特地区LUCC光谱分类精度研究

喀斯特地区复杂地表形态导致地面调查可深入性差、精度不高,遥感则作为该区有效监测与研究人类活动对土地利用(LUCC)方式与利用程度影响的主要手段。文章利用ALOS多光谱数据与Terra SARX的数据进行融合,讨论了HH极化微波后向散射数据用于改善多光谱遥感数据LUCC分类的精度,并比较了不同融合方法对地物识别。结果表明:2种数据之间的融合充分利用了多光谱的光谱信息与HH极化数据丰富的结构与纹理的特征,增强了不同地物之间的光谱差异,提高地物可分性;PC法融合、IHS法融合分类精度较单独使用ALOS多光谱数据分类精度分别提高了8%与13%,而且由于HH极化对植被含水量的敏感性,提高了"插花"分布的旱地与草地、林地等由植被覆盖的土地利用类型的区分精度。通过该研究探讨了HH极化数据与多光谱数据融合在地表信息提取中的应用,拓展了遥感数据在喀斯特地区土地利用领域应用的范围。

IMPROVING KARST REGION LUCC SOECTRA CLASSIFICATION ACCURACY BASED ON SAR

The surface morphology of Karst area is complex which causes difficult ground land investigation and low accuracy of investigation. Remote sensing is used as the main means of effective monitoring and studying human ac-tivity which impacts land use pattern and utilization degree. Combining ALOS multi spectral data with TerraSAR X polarization data, this paper discussed how the HH polarized microwave backscatter data was used to improve LUCC classification accuracy of the multi spectral remote sensing data. And then it compared the different fusion methods which were more suitable for every object to distinguish ground object. The results showed that: the combination with the two kinds of data could make full use of the characteristics of the spectral information of multi spectral da-ta, as well as the rich texture and structure information of HH polarization data, enhance the spectral differences a-mong different objects,and improve the distinguishable of ground features. Compared to the method of separately u-sing spectral data,the classification accuracy using the PC method and IHS method improved 8, 13 percentage, re-spectively. And the HH polarization improved the distinguish accuracy of "flower" distribution of dry land, grass-land, and woodland,because of the sensitivity of vegetation water content of HH. This research expanded the scope of application of remote sensing data in the field of land and resources and has the value of popularization.