大光斑激光测高的全波形数据分解是获取待测物体有效信息的关键环节。目前,大光斑激光测高的全波形数据分解方法主要采用高斯分解和小波分解。但是,对于不同地物反射的回波信号,这两种方法的分解效果与精度不明确。针对地球科学激光测高系统(GLAS)全波形数据,利用高斯分解与高斯小波基分解对平坦和斜坡区域中几种典型地物的波形数据进行分解,并采用最佳拟合优度与达到最佳拟合优度拟合次数等指标进行定性、定量比较与分析。实验结果表明:在最佳拟合优度上,高斯分解和高斯小波基分解数值接近,但随着地物复杂程度增加,高斯分解达到最佳拟合优度拟合的次数少于高斯小波基分解。

Full waveform data decomposition is critical to obtain the effective information of tested object from the large-spot laser altimetry data. Gaussian decomposition and wavelet decomposition are two universal methods to achieve the full waveform data decomposition in the large-spot laser altimetry currently. However, the decomposition effect and accuracy are ambiguity for different echo signals of ground objects. In this paper, the two methods are applied to the full waveform data of the Geoscience Laser Altimeter System (GLAS), and the waveforms of several typical ground objects in flat and slop areas are decomposed. The results are analyzed and compared qualitatively and quantitatively according to the index of goodness-of-fit and the iteration times of achieving the best goodness-of-fit. The results show that the Gaussian decomposition and Guassian wavelet decomposition are nearly same for the accuracy of best goodness-of-fit. With the increase of the complexity of the ground objects, the iteration times of achieving the best goodness-of-fit with the Gaussian decomposition is less than that with the Gaussian wavelet decomposition.