高光谱数据具有丰富的光谱特征,但是其空间分辨率相对较低。一些遥感数据具有与高光谱数据互补的优势,例如提供更精细的空间信息的高空间分辨率数据和具有高度信息的激光雷达LiDAR(Light Detection and Ranging)数据。通过将高光谱数据...高光谱数据具有丰富的光谱特征,但是其空间分辨率相对较低。一些遥感数据具有与高光谱数据互补的优势,例如提供更精细的空间信息的高空间分辨率数据和具有高度信息的激光雷达LiDAR(Light Detection and Ranging)数据。通过将高光谱数据与多源遥感数据进行融合,可以弥补高光谱数据空间分辨率相对较低,空间特征不够丰富的缺点。近年来,基于深度学习的方法已经在遥感数据分类研究中取得了一定的进展。然而,由于深度网络的特征提取过程是一个自主的过程,往往无法精确的获取最有利于遥感数据分类的特征;同时,深度学习方法具有复杂的网络结构和大量的参数,往往会在分类训练过程中造成参数拟合困难。以上这些因素会导致分类效果不佳。针对这些问题,本文提出了一种将卷积神经网络CNN(Convolutional Neural Network)和纹理特征相结合的多源遥感数据特征级融合分类框架。该方法共3个步骤,首先,对高光谱数据或多源遥感数据提取纹理特征;然后,构造CNN,分别将原始高光谱遥感数据、原始多源遥感数据和第一步中获得的纹理特征作为深度网络的输入进行深度特征提取;最后,将分别提取到的深度特征拼接,并利用Softmax分类器进行分类。为了验证本文提出方法的分类效果,本文在休斯顿和塞特福德矿地区公开数据集上进行实验,并将该分类框架与支持向量机分类方法、像素级融合分类方法和特征级融合分类方法进行对比。由此可以分析得出,本文提出的基于深度学习的融合分类方法可以获得较高的分类精度。展开更多
Based on the classic filter of progressive triangulated irregular network（TIN） densification, an improved filter is proposed in this paper. In this method, we divide ground points into grids with certain size and se...Based on the classic filter of progressive triangulated irregular network（TIN） densification, an improved filter is proposed in this paper. In this method, we divide ground points into grids with certain size and select the lowest points in the grids to reconstruct TIN in the process of iteration. Compared with the classic filter of progressive TIN densification（PTD）, the improved method can filter out attached objects, avoid the interference of low objects and obtain relatively smooth bare-earth. In addition, this proposed filter can reduce memory requirements and be more efficient in processing huge data volume. The experimental results show that the filtering accuracy and efficiency of this method is higher than that of the PTD method.展开更多
基金Supported by the National Natural Science Foundation of China(41301519)
文摘Based on the classic filter of progressive triangulated irregular network（TIN） densification, an improved filter is proposed in this paper. In this method, we divide ground points into grids with certain size and select the lowest points in the grids to reconstruct TIN in the process of iteration. Compared with the classic filter of progressive TIN densification（PTD）, the improved method can filter out attached objects, avoid the interference of low objects and obtain relatively smooth bare-earth. In addition, this proposed filter can reduce memory requirements and be more efficient in processing huge data volume. The experimental results show that the filtering accuracy and efficiency of this method is higher than that of the PTD method.
