Successful applications of machine learning for the analysis of remote sensing images remain limited by the difficulty of designing neural networks manually. However, while the development of neural architecture search offers the unique potential for discovering new and more effective network architectures, existing neural architecture search algorithms are computationally intensive methods requiring a large amount of data and computational resources and are therefore challenging to apply for developing optimal neural network architectures for remote sensing image classification. Our proposed method uses a differentiable neural architecture search approach for remote sensing image classification. We utilize a binary gate strategy for partial channel connections to reduce the sizes of the network parameters, creating a sparse connection pattern that lowers memory consumption and NAS computational costs. Experimental results indicate that our method achieves a 15.1% increase in validation accuracy during the search phase compared to DDSAS, although slightly lower (by 4.5%) than DARTS. However, we reduced the search time by 88% and network parameter size by 84% compared to DARTS. In the architecture evaluation phase, our method demonstrates a 2.79% improvement in validation accuracy over a manually configured CNN network.

For the real-time video streams over wireless broadcast network with differentiated terminal nodes, we consider the quality of communication link, the real-time transmission characteristics of video streams and the difference of terminal processing capability, then design an improved heuristic maximum weight clique algorithm based on expanding window (HMWC-EW) by using instantly decodable network coding (IDNC) technology. Then we use a heuristic algorithm with lower complexity to schedule the packet selection. Simulation results show that under the same system model, the performance of HMWC-EW improved algorithm can increase the percentage of terminal nodes that receive the required data successfully, thus realizing the differentiated transmission of real-time video streams efficiently compared to the existing IDNC algorithms.