With the development of information technology, multi-platform collaborative collection and processing of remote sensing images has become a significant trend. However, the existing models are challenging to achieve accurate and efficient image interpretation on remote sensing multi-platform systems. To solve this problem, we propose a novel distributed convolutional neural network (DCNNet) and demonstrate the superiority of our method in remote sensing image classification. Firstly, a progressive inference mechanism is introduced to support most images to be classified in advance with satisfactory accuracy, which minimises 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 terminals and the cloud network. Secondly, a multi-scale 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 multi-platform remote sensing image classification.