Target classification and recognition (TCR) are important information-extraction techniques for high-resolution remote sensing images (HRIs). However, because methods with high accuracy usually have higher time complexity, the massive remote sensing image has brought great difficulties for realtime application. In this article, we propose a hybrid, heterogeneous parallel processing algorithm to improve the efficiency TCR based on the Crossmodel Neural Cognitive Computing (CNCC) algorithm [parallel TCR based on MapReduce of hierarchical CNCC (PTCR-C)]. Parallel programming technologies used in this article include Open Multiprocessing (OpenMP), Compute Unified Device Architecture (CUDA), and the message passing interface (MPI). Experiments on public remote sensing image data sets show that the PTCR-C algorithm can effectively improve the efficiency of the original TCR algorithm. The PTCR-C algorithm has better augmented ability and provides the reference for further real-time TCR of remote sensing applications.

中文翻译：

---

高分辨率遥感图像的目标分类和识别：使用并行交叉模型神经认知计算算法

目标分类和识别 (TCR) 是高分辨率遥感图像 (HRI) 的重要信息提取技术。然而，由于精度高的方法通常具有较高的时间复杂度，海量的遥感影像给实时应用带来了很大的困难。在本文中，我们提出了一种基于交叉模型神经认知计算（CNCC）算法[基于分层 CNCC 的 MapReduce 的并行 TCR（PTCR-C）] 的混合、异构并行处理算法来提高效率 TCR。本文中使用的并行编程技术包括开放多处理 (OpenMP)、统一计算设备架构 (CUDA) 和消息传递接口 (MPI)。在公共遥感影像数据集上的实验表明，PTCR-C算法可以有效提高原有TCR算法的效率。PTCR-C算法具有更好的增强能力，为遥感应用的进一步实时TCR提供参考。