Advances in Hyperspectral Image Classification with a Bottleneck Attention Mechanism Based on 3D-FCNN Model and Imaging Spectrometer Sensor

Abstract

Deep learning approaches have significantly enhanced the classification accuracy of hyperspectral images (HSIs). However, the classification process still faces difficulties such as those posed by high data dimensions, large data volumes, and insufficient numbers of labeled samples. To enhance the classification accuracy and reduce the data dimensions and training needed for labeled samples, a 3D fully convolutional neural network (3D-FCNN) model was developed by including a bottleneck attention module. In such a model, the convolutional layer replaces the downsampling layer and the fully connected layer, and 3D full convolution is adopted to replace the commonly used 2D and 1D convolution operations. Thus, the loss of data in the dimensionality reduction process is effectively avoided. The bottleneck attention mechanism is introduced in the FCNN to reduce the redundancy of information and the number of labeled samples. The proposed method was compared to some advanced HSI classification approaches with deep networks, and five common HSI datasets were employed. The experiments showed that our network could achieve considerable classification accuracies by reducing the data dimensionality using a small number of labeled samples, thereby demonstrating its potential merits in the HSI classification process .