Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN
Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN
复制标题
使用双通道 CNN 进行多光谱 RGB-NIR 图像分类
DOI：
10.1109/access.2019.2896128
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Huang, Hui
中科院分区：
文献类型：
--
作者：
Jiang, Jionghui;Feng, Xi'an;Huang, Hui
随着4传感器线扫描相机技术的成熟，红色（R）、绿色（G）、蓝色（B）和近红外（RGB-NIR）数据集已经开始大量出现。RGB-NIR数据包含RGB图像的丰富颜色特征和NIR图像的尖锐边缘特征。目前，在许多研究中，RGB-NIR数据直接输入到处理算法中用于计算4D数据;在这些情况下，包含冗余信息，并且波段之间的高度相关性导致无法充分利用RGB-NIR数据的特性。在本文中，我们提出了一种双通道卷积神经网络（CNN）算法，该算法考虑到了航空图像中R，G和B波段之间的强相关性以及NIR波段与R，G和B波段之间的弱相关性。首先，在两个不同的CNN网络中计算RGB和NIR波段的特征，随后，在全连接层中执行特征融合。其次是分类。通过结合RGB-CNN和NIR-CNN两种神经网络，充分利用了RGB-NIR数据的各自特性。
As 4-sensor line scan camera technology has matured, red (R), green (G), blue (B), and near-infrared (RGB-NIR) datasets have begun to appear in large numbers. The RGB-NIR data contain the rich color features of the RGB image and the sharp edge features of the NIR image. At present, in many studies, the RGB-NIR data are input directly into the processing algorithms for calculation of the 4D data; in these cases, redundant information is included, and the high correlation between the bands results in an inability to fully exploit the characteristics of the RGB-NIR data. In this paper, we propose a double-channel convolutional neural network (CNN) algorithm that takes into account the strong correlation between the R, G, and B bands in aerial images and the weaker correlation between the NIR band and the R, G, and B bands. First, the features of the RGB and NIR bands are calculated in two different CNN networks, and subsequently, feature fusion is performed in the fully connected layer. This is followed by the classification. By combining the two neural networks of RGB-CNN and NIR-CNN, the respective characteristics of the RGB-NIR data are fully exploited.