Land-cover Classification Using ASTER Multi-band Combinations Based on Wavelet Fusion and SOM Neural Network
Land-cover Classification Using ASTER Multi-band Combinations Based on Wavelet Fusion and SOM Neural Network
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DOI：
10.14358/pers.74.3.333
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发表时间：
2008-03
影响因子：
1.3
通讯作者：
H. Bagan;Qinxue Wang;Masataka Watanabe;S. Kameyama;Y. Bao
中科院分区：
文献类型：
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作者：
H. Bagan;Qinxue Wang;Masataka Watanabe;S. Kameyama;Y. Bao
在本研究中，我们利用基于小波融合和自组织映射（SOM）神经网络方法的先进星载热发射和反射辐射计（ASTER）可见光近红外（VNIR）、短波红外（SWIR）和热红外（TIR）波段组合开发了一种土地覆盖分类方法，并比较了ASTER多波段数据不同组合的分类精度。名为 ARSIS（Amelioration de la Resolution Spatiale par Injection de Structures）的小波融合概念用于在预处理阶段融合 ASTER 数据。为了将小波融合方法应用于ASTER数据，计算了ASTER VNIR数据的主成分。第一主成分用作小波融合的基础图像。在我们的实验中，ASTER VNIR、SWIR 和 TIR 数据的空间分辨率调整为相同的 15 m。通过这种融合，SOM 分类准确率从 83% 提高到 93%，并且分类准确率随着条带数量的增加而提高。当使用所有 14 个波段时，分类精度达到最高值，但当使用 3 个 VNIR 波段、3 个 SWIR 波段和 2 个 TIR 波段时，分类精度接近最高值。最大似然分类（MLC）方法也得到了类似的趋势，但MLC对所有波段组合的分类精度明显低于SOM方法获得的分类精度。
In this study, we developed a land-cover classification methodology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) band combinations based on wavelet fusion and the selforganizing map (SOM) neural network methods, and compared the classification accuracies of different combinations of ASTER multi-band data. A wavelet fusion concept named ARSIS (Amelioration de la Resolution Spatiale par Injection de Structures) was used to fuse ASTER data in the preprocessing stage. In order to apply the wavelet fusion method to ASTER data, the principal components of ASTER VNIR data were computed. The first principal component was used as the base image for wavelet fusion. In our experiments, the spatial resolution of ASTER VNIR, SWIR, and TIR data was adjusted to the same 15 m. SOM classification accuracy was increased from 83 percent to 93 percent by this fusion, and classification accuracy increased along with the increase of band numbers. Classification accuracy reaches the highest value when all 14 bands are used, but classification accuracy closely approached the highest value when three VNIR bands, three SWIR bands, and two TIR bands were used. A similar tendency was also obtained by the maximum likelihood classification (MLC) method, but the classification accuracies of MLC over all band combinations were considerably obviously lower than those obtained by the SOM method.