基于SFIM算法的融合影像分类研究

基金项目: 国家自然科学基金资助项目(40371107);国家教育部高等学校骨干教师专项基金资助项目。

详细信息

Classification of Fused Imagery Base on the SFIM Algorithm

College of Environment and Resources, Fuzhou University, 523 Gongye Road, Fuzhou 350002, China

摘要: 以福州市城乡结合部的Landsat7 ETM⁺影像为例,就该融合算法的自动分类精度作进一步研究,并藉此对该算法作全面评价。研究结果表明,SFIM融合影像的分类精度高于原始未融合影像的分类精度,但选择不同尺寸的均值滤波器会影响融合影像的分类精度。试验表明,太大尺寸的滤波器虽然能提高高分辨率影像的信息融入度,但会降低融合影像的分类精度和光谱的保真度。
- SFIM算法 /
- 融合影像分类 /
- 遥感
Abstract: This paper aims to further study the classification accuracy of the SFIM-fused imagery based on a Landsat 7 ETM⁺ sub-scene covering the urban fringe of southeastern Fuzhou City. The study reveals that the classification accuracy of the SFIM-fused image is higher than that of the original image. Nevertheless, the difference in smoothing filter kernel sizes used in producing the SFIM-fused images can affect the classification accuracy. Too large smoothing filter kernel size will decrease classification accuracy as well as spectral fidelity in spite of increasing spatial frequency information absorption. Using a 5×5 smoothing filter has achieved the highest classification accuracy in this case study.
- SFIM algorithm /
- classification accuracy of fused-imagery /
- remote sensing