DCNNet: A Distributed Convolutional Neural Network for Remote Sensing Image Classification
Ting Zhang, Zhirui Wang, Peirui Cheng, Guangluan Xu, Xian Sun
Abstract
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.