DCNNet: A Distributed Convolutional Neural Network for Remote Sensing Image Classification

With the development of information technology, multiplatform collaborative collection and processing of remote sensing (RS) images has become a significant trend. However, the existing models are challenging to achieve accurate and efficient image interpretation on RS multiplatform systems. To solve this problem, we propose a novel distributed convolutional neural network (DCNNet) and demonstrate the superiority of our method in RS image classification. First, a progressive inference mechanism is introduced to support most images to be classified in advance with satisfactory accuracy, which minimizes redundant cloud transmission and achieves higher inference acceleration. Meanwhile, a distributed self-distillation paradigm is designed to integrate and refine in-depth features, performing efficient knowledge transfer between the terminals and the cloud network. Second, a multiscale feature fusion (MSFF) module is presented to extract valid receptive fields and assign weights to crucial channel dimension features. Finally, a sampling augmentation (SA) attention is proposed to enhance the effective feature representation of RS images through a bottom-up and top-down feedforward structure. We conducted extensive experiments and visual analyses on three benchmark scene classification datasets and one fine-grained dataset. Compared with the existing methods, DCNNet consolidates several advantages in terms of accuracy, computation, transmission, and processing efficiency into a single framework for multiplatform RS image classification.