基于单亲遗传算法和子像元/像元空间吸引模型的亚像元制图研究
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摘要
混合像元普遍存在于遥感图像数据中。与传统的硬分类(Hard Classification)方法相比,在处理混合像元时,软分类(Soft Classification)技术可以避免信息丢失;但是,通过软分类技术获得的结果,仍然无法确定各分类在像元中的具体位置。子像元制图(或超分辨率制图、亚像元制图)技术能将软分类技术得到的结果转化为更高分辨率的图像,它能兼得软分类和硬分类两者的优势。将遗传算法的一个变种—单亲遗传算法应用于子像元制图,结合子像元/像元空间吸引模型,单亲遗传算法能直接获得子像元制图结果。以合成的图像和实际的土地覆盖图像为实验对象,通过目视比较和定量精度评价,与硬分类的结果相比,该方法能取得更高的制图精度和更好的结果。
Mixed pixels are widely presented in remotely sensed images.Soft classification techniques can avoid the loss of information comparing to hard classification methods while handling mixed pixels.However,the assignment to these classes by soft classification does not specify the location in the pixel.Sub-pixel mapping(or super-resolution mapping)is a technique which designed to use the information obtained by soft classification to get a sharpened image and it can incorporate benefits of both hard and soft classification techniques.In this paper,a variation of genetic algorithm,named as partheno-genetic algorithm(PGA),is developed to accomplish the sub-pixel mapping.With the sub-pixel/pixel attraction model,PGA can achieve sub-pixel mapping in a straightforward one-pass process.It is evaluated with artificial and degraded land cover images by visual and quantitative classification accuracy indices.The results show this method can increase accuracy while compared to hard classification.
Mixed pixels are widely presented in remotely sensed images.Soft classification techniques can avoid the loss of information comparing to hard classification methods while handling mixed pixels.However,the assignment to these classes by soft classification does not specify the location in the pixel.Sub-pixel mapping(or super-resolution mapping)is a technique which designed to use the information obtained by soft classification to get a sharpened image and it can incorporate benefits of both hard and soft classification techniques.In this paper,a variation of genetic algorithm,named as partheno-genetic algorithm(PGA),is developed to accomplish the sub-pixel mapping.With the sub-pixel/pixel attraction model,PGA can achieve sub-pixel mapping in a straightforward one-pass process.It is evaluated with artificial and degraded land cover images by visual and quantitative classification accuracy indices.The results show this method can increase accuracy while compared to hard classification.
引文
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[2]Mertens K C,Verbeke L P C,Ducheyne E I,et al.Using Genetic Algorithms in Sub-pixel Mapping[J].International Journal of Remote Sensing,2003,24(21):4241-4247.
[3]Atkinson P M,Cutler M E J,Lewis H.Mapping Sub-pixel Propor-tional Land Cover with AVHRR Imagery[J].International Journal of Remote Sensing,1997,18(4):917-935.
[4]Atkinson P M.Super-resolution Target Mapping from Softclassified Remotely Sensed Imagery[C]//Proceedings of the6th International Conference on Geocomputation.University of Leeds,Leeds,UK,2001:1-10.
[5]Atkinson P M.Sub-pixel Target Mapping from Soft-classified,Remotely Sensed Imagery[J].Photogrammetric Engineering and Remote Sensing,2005,71(7):839-846.
[6]Verhoeye J,Wulf D R.Land Cover Mapping at Sub-pixel Scales Using Linear Optimization Techniques[J].Remote Sensing of Environment,2002,79(1):96-104.
[7]Aplin P,Atkinson P M.Sub-pixel Land Cover Mapping for Per-field Classification[J].International Journal of Remote Sensing,2001,22(14):2853-2858.
[8]Tatem A J,Lewis H G,Atkinson P M,et al.Super-resolution Land Cover Pattern Prediction Using a Hopfield Neural Network[J].Remote Sensing of Environment,2002,79(1):1-14.
[9]Thornton M W,Atkinson P M,Holland D A.Sub-pixel Mapping of Rural Land Cover Objects from Fine Spatial Resolution Satellite Imagery Using Super-resolution Pixel-swapping[J].International Journal of Remote Sensing,2006,27(3):473-491.
[10]Mertens K C,Verbeke L P C,Westra T,et al.Sub-pixel Mapping and Sub-pixel Sharpening Using Neural Network Predicted Wavelet Coefficients[J].Remote Sensing of Environment,2004,91(2):225-236.
[11]Kasetkasem T,Arora M K,Varshney P K.Super-resolution Land Cover Mapping Using a Markov Random Field based Approach[J].Remote Sensing of Environment,2005,96(3-4):302-314.
[12]Nguyen M Q,Atkinson P M,Lewis H G.Superresolution Mapping Using a Hopfield Neural Network with Fused Images[J].IEEE Transactions on Geosience and Remote Sensing,2006,44(3):736-749.
[13]Li M J,Fan S S,Luo A.A Partheno-genetic Algorithm for Com-binatorial Optimization[J].Lecture Notes in Computer Science,2004,3316:224-229.
[14]Bai J,Chang H,Yi Y.An Partheno-genetic Algorithm for Multi-dimensional Knapsack Problem[C]//Proceedings of the Fourth International Conference on Machine Learning and Cybernetics.Guangzhou,China,2005:2962-2965.
[15]Mertens K C,Baets B D,Verbeke L P C,et al.A Sub-pixel Mapping Algorithm based on Sub-pixel/pixel Spatial Attraction Models[J].International Journal of Remote Sensing,2006,27(15):3293-3310.