Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification

Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have been developed to enhance and extract the spatially and spectrally semantic information in the hyperspectral image (HSI) and light detection and ranging (LiDAR) data processing and analysis. However, an abundance of redundant information and sometimes a lack of discriminative features reduce the efficiency and effectiveness of multisource classification methods. This article proposes a fractional Gabor convolutional network (FGCN), focusing on efficient feature fusion and comprehensive feature extraction. First, the proposed FGCN uses Octave convolution layers to perform multisource information fusion and preserve discriminative information. Second, fractional Gabor convolutional (FGC) layers are proposed to extract multiscale, multidirectional, and semantic change features. The completeness and discrimination of the multisource features using different FGC kernels are improved, which yield robust feature extraction against semantic changes. Finally, the fractional Gabor feature and spectral feature are combined with two weighting factors which can be learned during the network training. Experimental results and comparisons with state-of-the-art multisource classification methods indicate the effectiveness of the proposed FGCN. With the FGCN, we can obtain an 89.90x0025; overall accuracy on the challenging Muufl Gulfport (MUUFL) data set, with an improvement of 3x0025; over state-of-the-art methods.

Automated summary

This article proposes a fractional Gabor convolutional network (FGCN) for efficient feature fusion and comprehensive feature extraction. The FGCN uses Octave convolution layers for multisource information fusion and fractional Gabor convolutional (FGC) layers for extracting multiscale, multidirectional, and semantic change features. Experimental results demonstrate the effectiveness and superiority of the proposed FGCN in multisource classification tasks.