基于一阶梯度信息的光谱相似度评价方法

目前的光谱相似度评价方法主要基于光谱形状和幅值两种信息，但这两种信息仅仅能体现出光谱的轮廓，并不能很好的反应地物光谱的吸收峰等“指纹”特征，为了更好的体现出光谱特征在评价中的作用，提出了基于一阶梯度信息的光谱相似度评价方法。首先对传统光谱角度匹配度评价方法SAM进行了改进，提出MSAM评价方法，进而提出了调整的梯度光谱角度匹配(MGSAM)法。MGSAM比较了两条光谱曲线的梯度角匹配度，光谱曲线的梯度信息可以突出光谱吸收峰等“指纹”特性的存在，因此MGSAM可以充分体现出两条对比曲线的光谱特征相似度。分析了偏置信息和光谱深度对于MSAM和MGSAM的影响，指出MGSAM对于偏置信息具有更强的鲁棒性，且可以客观地反映出光谱深度差异，进而直观地反映出光电系统或相关算法的光谱特征保真能力。将MGSAM作为评价方法应用到压缩感知光谱成像系统评价中，仿真结果表明，随着采样率的变化，MSAM的值在 0.998~1之间，而MGSAM的值在0.72~1之间，具有明显的变化并具有较大的差异性，可以客观地反映出压缩感知系统对于光谱特征的保真能力，并具有更强的差异化分辨力，为该类系统提供了一个更客观的评价方法。将MGSAM应用到了基于光谱相似度的地物分类中，测试数据选择了Salinas，Pavia和Indian Pines三个公开数据，结果显示基于MSAM的平均分类精度为0.86，基于MGSAM的平均分类精度0.93，由此说明MGSAM可以突出光谱特征在分类中的作用，大大提高了分类精度。

Method for Evaluating Spectral Similarity Based on First-Order Gradient Information

Current evaluation methods for spectral similarity are mainly based on the shapes and amplitudes of spectra,but these two can only reflect the outline information of spectra,and cannot well reflect the fingerprint characteristics of spectra of ground objects.In order to better embody the application of spectral characteristics in the evaluation,it is proposed herein a method for evaluating spectral similarity based on first-order gradient information.Firstly,it is proposed an MSAM similarity evaluation method,further,a modified gradient spectral angle matching(MGSAM)method by adjusting the traditional spectral angle similarity evaluation method SAM.MGSAM compares the gradient angle matching degree of the two spectral curves.The gradient information of the spectral curves can highlight the existence of"fingerprint"characteristics such as spectral absorption peaks,so MGSAM can fully reflect the similarity of the spectral characteristics of the two contrast curves.By analyzing the influence of offset information and spectral depth on MSAM and MGSAM,it is pointed out that MGSAM has stronger robustness to offset information,and can objectively reflect the difference in spectral depth,so as to directly reflect the fidelity of spectral features in the photoelectric systems or related algorithms.By applying MGSAM as the evaluation method to the evaluation of compressed sensing imaging system,the simulation results showed that as the change of sampling rate,the MSAM values ranged between 0.998~1,while MGSAM values ranged between 0.72~1,with obvious change and great difference.It objectively reflects the fidelity ability of the compressed sensing system for spectral features and has a stronger differentiation ability,thereby providing a more objective evaluation method for such systems.By applying MGSAM to the classification of ground objects based on spectral similarity,and selecting Salinas,Pavia and Indian Pines for the test data,the results showed that the average classification accuracy based on MSAM was 0.86,while that based on MGSAM was 0.93.This shows that MGSAM can highlight the role of spectral features in classification and greatly improve the classification accuracy.