论文标题
使用深卷积网络基于选择性内核机制的更快的高光谱图像分类
Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks
论文作者
论文摘要
高光谱图像富含空间和光谱信息。使用3D-CNN可以同时获得空间和光谱维度的特征，以促进特征的分类，但高光谱图像信息频谱维度信息冗余。连续3D-CNN的使用将导致大量参数，并且设备的计算能力要求很高，并且训练需要太长。这封信设计了更快的选择性内核机构网络（FSKNET），FSKNET可以平衡此问题。它使用3D-CNN设计了3D-CNN和2D-CNN转换模块，同时降低了空间和频谱的维度，以完成特征提取。但是，这样的模型还不够轻巧。在转换后的2D-CNN中，提出了一种选择性内核机制，该机制允许每个神经元根据双向输入信息量表调整接受场大小。在选择性内核机制下，它主要包括两个组件，即SE模块和可变卷积。 SE获取通道维度注意力和可变卷积，以获得地面对象的空间维度变形信息。该模型更准确，更快且计算较少。 FSKNET在IN，UP，Salinas和Botswana数据集上具有很小的参数的高精度。
Hyperspectral imagery is rich in spatial and spectral information. Using 3D-CNN can simultaneously acquire features of spatial and spectral dimensions to facilitate classification of features, but hyperspectral image information spectral dimensional information redundancy. The use of continuous 3D-CNN will result in a high amount of parameters, and the computational power requirements of the device are high, and the training takes too long. This letter designed the Faster selective kernel mechanism network (FSKNet), FSKNet can balance this problem. It designs 3D-CNN and 2D-CNN conversion modules, using 3D-CNN to complete feature extraction while reducing the dimensionality of spatial and spectrum. However, such a model is not lightweight enough. In the converted 2D-CNN, a selective kernel mechanism is proposed, which allows each neuron to adjust the receptive field size based on the two-way input information scale. Under the Selective kernel mechanism, it mainly includes two components, se module and variable convolution. Se acquires channel dimensional attention and variable convolution to obtain spatial dimension deformation information of ground objects. The model is more accurate, faster, and less computationally intensive. FSKNet achieves high accuracy on the IN, UP, Salinas, and Botswana data sets with very small parameters.
