Dependable land cover data are required to aid in the resolution of a broad spectrum of environmental issues. Land cover classification at a broad scale has been carried out using data from traditional ground‐based information from the Advanced Very High‐Resolution Radiometer. From the merits as well as demerits of the existing works discussed in the literature, this article seeks to establish a novel technique for automatic, fast, as well as precise land cover classification deploying remote sensing data. The proposed approach follows feature extraction and classification stages. From input information, the statistical characteristics are extracted as well as they are subjected to classification via optimized deep convolutional neural network. Particularly, the convolutional layers are optimized for by means of a new Proposed Lion Algorithm with a new Cub pool Update (PLACU) approach. The established model is the advanced level of the standard lion algorithm. The superiority of the established technique is determined by the extant techniques regarding positive and negative metrics. The accuracy of the work that is being presented (PLACU) is superior to the existing methods like Dragonfly algorithm, Jaya algorithm, sea lion optimization, and lion algorithm techniques by 20%, 15%, 112%, and 10%, respectively.

中文翻译：

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通过改进的狮子算法优化卷积神经网络进行土地覆盖分类

需要可靠的土地覆盖数据来帮助解决广泛的环境问题。使用先进甚高分辨率辐射计的传统地面信息数据进行了大范围的土地覆盖分类。根据文献中讨论的现有工作的优点和缺点，本文试图建立一种利用遥感数据自动、快速、精确地进行土地覆盖分类的新技术。所提出的方法遵循特征提取和分类阶段。从输入信息中提取统计特征，并通过优化的深度卷积神经网络对它们进行分类。特别是，卷积层通过新提出的 Lion 算法和新的 Cub 池更新 (PLACU) 方法进行了优化。建立的模型是标准狮子算法的先进水平。已建立技术的优越性是由有关积极和消极指标的现有技术决定的。所展示的工作（PLACU）的准确性比现有方法（例如 Dragonfly 算法、Jaya 算法、海狮优化和 Lion 算法技术）分别优于 20%、15%、112% 和 10%。