基于分数阶空谱联合Gabor特征的高光谱遥感图像分类  

Fractional Gabor-based feature for hyperspectral image classification

作　　者：王亚丽 汤定定 李丙春 要秀宏 贾森[2] WANG Yali;TANG Dingding;LI Bingchun;YAO Xiuhong;JIA Sen(College of Computer Science and technology,Kashi University,Kashi Xinjiang 844000,China;College of Computer Science and Software Engineering,Shenzhen University,Shenzhen Guangdong 518000,China Abstract:In order to fully consider the spatial spectral structure features of hyperspectral remote sensing images)

机构地区：[1]喀什大学计算机科学与技术学院,新疆喀什844000 [2]深圳大学计算机与软件工程学院,广东深圳518000

出　　处：《激光杂志》2024年第6期144-150,共7页Laser Journal

基　　金：高校科研计划项目青年项目(No.XJEDU2022P080);喀什地区科技计划项目(No.KS2022083)。

摘　　要：为充分考虑高光谱遥感图像的空谱结构特征,降低数据冗余,获取更具识别性的特征,提高分类精度。提出一种基于分数阶Gabor的高光谱图像分类方法,在分数域实现对局部信号的多分辨分析,以增强对高光谱图像的表征能力。首先,通过设置多阶正弦波构建多组分数阶Gabor滤波器,获得有效的特征表达。其次,对Gabor相位特征进行象限位编码,并通过汉明距离计算码距,降低计算复杂度。最后,融合不同阶的Gabor相位特征从而得到互补的纹理信息,以获取更高的分类性能。基于Trento真实数据集,选择3个分类样本进行训练,总体分类精度达到87.15%,Kappa系数为0.83,实验结果验证了该方法在小样本训练情况下的有效性,对比其他算法,提高了分类精度。In order to fully consider the spatial spectral structure features of hyperspectral remote sensing images,reduce data redundancy,obtain more recognizable features and improve classification accuracy.In this paper,we propose a fractional-order Gabor-based hyperspectral image classification method,which implements multi-resolution analysis of local signals in the fractional domain to enhance the characterisation of hyperspectral images.Firstly,a multiple component fractional-order Gabor filter is constructed by setting up a multiple order sine wave to obtain an effective feature representation.Secondly,the Gabor phase features are encoded by quadrant bits,and the code distance is calculated by Hamming distance,which reduces the computational complexity.Finally,the Gabor phase features of different orders are fused to obtain complementary texture information in order to obtain higher classification performance.Based on the Trento real dataset,three classification samples were selected for training.The overall classification accuracy reached 87.15%,and the Kappa coefficient was 0.83.The experimental results have verified the effectiveness of this method in small sample training,and compared with other algorithms,it has improved classification accuracy.

关 键 词：高光谱图像分类 GABOR滤波器 分数阶滤波器 

分 类 号：TN249[电子电信—物理电子学]