Advanced sensor tech integrates into diverse applications, including remote sensing, robotics, and IoT. Combining artificial intelligence (AI) with sensors enhances their capabilities, creating smart sensors, revolutionizing remote sensing and Internet of Things (IoT). This synergy forms a potent technology in the field. This study carries out a comprehensive analysis of the progress made in Hyperspectral sensors and AI-based classification techniques that are employed in remote sensing fields that utilize hyperspectral images. The classification of images obtained from Hyperspectral Sensors (HSS) has emerged as a prominent research subject within the domain of remote sensing. HSS offer a wealth of information across numerous spectral bands, supporting diverse applications such as land cover classification, environmental monitoring, agricultural assessment, change detection, and more. However, the abundance of data present in HSS also poses the challenge called the curse of dimensionality. The reduction of data dimensionality is crucial before applying any machine learning model to achieve optimal results. The present study introduces a new hybrid strategy combining the Back-Propagation algorithm with a variable adaptive momentum (BPVAM) and principal component analysis (PCA) for the purpose of classifying hyperspectral images. PCA is first applied to obtain an optimal set of discriminative features by eliminating highly correlated and redundant features. These features are then fed into the BPVAM model for classification. The addition of the momentum term in the weight update equation of the backpropagation algorithm helped achieve faster convergence with high accuracy. The proposed model was subjected to evaluation through experiments conducted on two benchmark datasets. These results indicated that the hybrid model based on BPVAM with PCA is an efficient technique for HSS classification.

先进的传感器技术集成到各种应用中，包括遥感、机器人和物联网。将人工智能 (AI) 与传感器相结合可增强其功能，创建智能传感器，彻底改变遥感和物联网 (IoT)。这种协同作用形成了该领域的一项强大技术。本研究对高光谱传感器和基于人工智能的分类技术所取得的进展进行了全面分析，这些技术应用于利用高光谱图像的遥感领域。从高光谱传感器（HSS）获得的图像分类已成为遥感领域的一个重要研究课题。HSS 提供跨多个光谱带的丰富信息，支持土地覆盖分类、环境监测、农业评估、变化检测等多种应用。然而，HSS 中存在的大量数据也带来了称为维数灾难的挑战。在应用任何机器学习模型以获得最佳结果之前，降低数据维度至关重要。本研究引入了一种新的混合策略，将反向传播算法与可变自适应动量（BPVAM）和主成分分析（PCA）相结合，用于对高光谱图像进行分类。首先应用 PCA 通过消除高度相关和冗余的特征来获得最佳的判别特征集。然后将这些特征输入 BPVAM 模型进行分类。在反向传播算法的权重更新方程中添加动量项有助于实现更快的收敛速度和高精度。通过在两个基准数据集上进行的实验对所提出的模型进行了评估。这些结果表明，基于 BPVAM 和 PCA 的混合模型是一种有效的 HSS 分类技术。