孙立新, 高文. 基于粗糙集和遗传算法的超光谱波段集合整体缩减[J]. 武汉大学学报 ( 信息科学版), 1999, 24(4): 306-311.
引用本文: 孙立新, 高文. 基于粗糙集和遗传算法的超光谱波段集合整体缩减[J]. 武汉大学学报 ( 信息科学版), 1999, 24(4): 306-311.
Sun Lixin, Gao Wen. Hyperspectral Band Set Global Reduction Based on Rough Sets and Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 1999, 24(4): 306-311.
Citation: Sun Lixin, Gao Wen. Hyperspectral Band Set Global Reduction Based on Rough Sets and Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 1999, 24(4): 306-311.

基于粗糙集和遗传算法的超光谱波段集合整体缩减

Hyperspectral Band Set Global Reduction Based on Rough Sets and Genetic Algorithm

  • 摘要: 提出一种对高光谱遥感影像波段集合进行整体缩减的方法。该方法首先根据模糊集和粗糙集理论,对原光谱波段集会进行近似等价波段区间的自动划分。对于每个近似等价波段区间,在考虑其他区间合成影像数据影响的情况下,利用遗传算法进行合成权系数的优化。实验表明,整体迭代线性合成方法不仅具有较高的计算效率,而且可以获得比独立线性合成方法明显优化的结果。

     

    Abstract: In this paper,a rough sets based global reduction approach,which is suitable for imaging spectrometer image is proposed.According to the fuzzy sets and rough sets theory,the set of original bands is first automatically divided into several approximative equivalent intervals. In each interval, equivalent bands are linearly combined into one by using genetic algorithm under the consideration of the combined images of other equivalent intervals.Experimental results indicate that the global reduction approach is not only relative faster, but also better than independent reduction approaches.Taking the global reduction as the preprocessing step of imaging spectrometer images, we can apply traditional remote sensing image classification approaches to this type of remote sensing image data.

     

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