Information fusion for rural land-use classification with high-resolution satellite imagery
Information fusion for rural land-use classification with high-resolution satellite imagery
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DOI：
10.1109/tgrs.2003.810707
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发表时间：
2003-06
期刊：
影响因子：
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通讯作者：
Wanxiao Sun;Volker Heidt;P. Gong;Gang Xu
中科院分区：
文献类型：
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作者：
Wanxiao Sun;Volker Heidt;P. Gong;Gang Xu
我们提出了一种信息融合方法提取的土地利用信息的全色和多光谱印度遥感卫星1C（IRS-1C）卫星图像的基础上。它集成了图像中存在的光谱、空间和结构信息。首先制作了一幅专题地图，并对多光谱图像进行了最大似然分类。然后对专题地图进行概率松弛（PR），以利用邻近信息改进分类。此外，我们将边缘提取的高分辨率全色图像的分类。利用边缘检测、边缘阈值化和边缘细化等操作生成边缘图。最后，一个改进的区域增长的方法来改善图像分类。该程序证明是更有效的土地利用分类比传统的方法只基于多光谱数据。改进后的土地利用图具有区域间边界清晰、混合像元数量减少和区域同质性增强等特点。整体kappa统计值从融合前的0.52显著增加至融合后的0.75。
We propose an information fusion method for the extraction of land-use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery. It integrates spectral, spatial and structural information existing in the image. A thematic map was first produced with a maximum-likelihood classification (MLC) applied to the multispectral imagery. Probabilistic relaxation (PR) was then performed on the thematic map to refine the classification with neighborhood information. Furthermore, we incorporated edges extracted from the higher resolution panchromatic imagery in the classification. An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was used to improve image classification. The procedure proved to be more effective in land-use classification than conventional methods based only on multispectral data. The improved land-use map is characterized with sharp interregional boundaries, reduced number of mixed pixels and more homogeneous regions. The overall kappa statistics increased considerably from 0.52 before the fusion to 0.75 after.