Accuracy and computational time are two crucial parameters influencing the efficacy of classification algorithms for remote sensing applications. Machine learning algorithms are known for achieving notable success for several classification problems in various domains, including remote sensing. However, they are well-recognized and considered accurate and efficient for closed-set recognition (CSR) but may provide suboptimal and erroneous results for open-set recognition (OSR) tasks. Many practical image-driven and computer vision applications have open-set and dynamic scenarios with unknown data where classification algorithms have not yet achieved significant prediction performance. This paper presents a group of class-aware (CA) classification algorithms based on a supervised cascaded classifier system (SC²S), called CA-SC²S, which is accurate for OSR and CSR tasks. We evaluate the prediction accuracy of the proposed methods against the state-of-the-art methods in a multiclass setting using multiple image classification scenarios of OSR and CSR. The test case scenarios use six multispectral and hyperspectral datasets from different sensing platforms. And to assess the computational performance of the methods, we designed various field-programmable gate array (FPGA) architectures of the proposed methods. We evaluated their real-time performance on a low-cost, low-power Artix-7 35 T FPGA.

精度和计算时间是影响遥感应用分类算法有效性的两个关键参数。机器学习算法以在包括遥感在内的各个领域的几个分类问题上取得显着成功而闻名。然而，它们是公认的并且被认为对于封闭集识别（CSR）是准确和有效的，但可能为开放集识别（OSR）任务提供次优和错误的结果。许多实际的图像驱动和计算机视觉应用程序具有开放集和动态场景，其中分类算法尚未实现显着的预测性能。本文提出了一组基于监督级联分类器系统（SC ²S)，称为 CA-SC ² S，对于 OSR 和 CSR 任务是准确的。我们使用 OSR 和 CSR 的多个图像分类场景在多类设置中评估所提出的方法与最先进的方法的预测准确性。测试用例场景使用来自不同传感平台的六个多光谱和高光谱数据集。为了评估这些方法的计算性能，我们设计了所提出方法的各种现场可编程门阵列 (FPGA) 架构。我们在低成本、低功耗的 Artix-7 35 T FPGA 上评估了它们的实时性能。