Hyperspectral remote sensing is one of the important approaches in the area of remote sensing owing to the latest enhancements in the Hyper Spectral Imaging (HSI) technology. The classification represents a direct approach in the HSI field that provides every pixel a particular semantic label based on its behavior automatically. Nowadays, deep learning-oriented techniques have gained wide attention in the area of HSI classification. Although Convolutional Neural Network (CNN)-oriented techniques are subjected to the HSI classification, their performances are not up to the expectation. This is because; the majority of the traditional techniques cannot utilize the intrinsic behavior of distinct pixels in HSI efficiently. The inherent relationships are ignored between distinct category dependencies, distinct spectral bands, and distinct spatial pixels. Moreover, deep learning methods are required to build a huge and complicated network, and hence the training process tends to be time-consuming. Hence, these work tactics to design and implement a novel HSI classification method using deep structured architectures. The major stages of the offered method are feature extraction, and classification. Initially, the HSI from RIT-18 benchmark sources is collected. Before processing it for feature extraction, the images are split into ‘n’ number of patches, where the length of every patch is 16 × 16. Then the feature extraction begins, in which the spatial and spectral features, as well as the Enhanced CNN, are utilized for acquiring the deep features from the entire patches. In the CNN, the architecture is enhanced by the “Modified Velocity-based Colliding Bodies Optimization (MV-CBO)”. Then, the entire features acquired from all the patches are concatenated. Finally, the utilization of deep structured architectures termed modified Recurrent Neural Network (RNN) is utilized in the classification phase, which classifies the images into different categories as per the dataset. The RNN architecture is also modified by the MV-CBO to attain high classification accuracy. From the simulation results, the accuracy rate of the MV-CBO-M-RNN at a 75% learning rate is correspondingly secured at 3.16%, 4.26%, 1.03%, and 2.08% more advanced than DNN, RNN, CNN, and NN. The validation of the recommended technique on challenging public datasets, and the experimental evaluation over baseline approaches validates the efficiency and robustness of the suggested model.

由于高光谱成像（HSI）技术的最新增强，高光谱遥感成为遥感领域的重要方法之一。该分类代表了 HSI 领域的一种直接方法，它根据每个像素的行为自动为其提供特定的语义标签。如今，面向深度学习的技术在 HSI 分类领域受到广泛关注。尽管面向卷积神经网络（CNN）的技术受到HSI分类，但其性能并未达到预期。这是因为; 大多数传统技术无法有效利用 HSI 中不同像素的固有行为。不同类别依赖性、不同光谱带和不同空间像素之间的内在关系被忽略。此外，深度学习方法需要构建庞大而复杂的网络，因此训练过程往往非常耗时。因此，这些工作策略是使用深层结构化架构来设计和实现一种新颖的 HSI 分类方法。所提供方法的主要阶段是特征提取和分类。最初，从 RIT-18 基准源收集 HSI。在进行特征提取之前，图像被分成“n”个块，每个块的长度为 16 × 16。然后开始特征提取，其中空间和光谱特征以及增强型 CNN ，用于从整个补丁中获取深层特征。在 CNN 中，该架构通过“基于修改速度的碰撞体优化（MV-CBO）”得到增强。然后，将从所有补丁获取的全部特征连接起来。最后，在分类阶段使用被称为改进的循环神经网络（RNN）的深层结构化架构，根据数据集将图像分类为不同的类别。RNN 架构也经过 MV-CBO 的修改，以获得高分类精度。从仿真结果来看，MV-CBO-M-RNN在75%学习率下的准确率分别比DNN、RNN、CNN和NN先进3.16%、4.26%、1.03%和2.08% 。对具有挑战性的公共数据集的推荐技术的验证以及对基线方法的实验评估验证了建议模型的效率和稳健性。