Scholarlink

<p>Remote sensing (RS) image interpretation includes multi-label picture classification, a key problem. The complicated and diverse character of many remote sensing scenes, which are produced by the spatial combination and association of various objects, makes classification difficult. In this research, an innovative approach that combines deep neural networks with bio-inspired optimization techniques to handle the challenges of classifying remote sensing images. First, the efficacy of proposed model was evaluated using the publicly accessible remote sensing scene dataset for multi-label classification. Second, this work employs the HHO (Harris Hawk optimization) model to improve classification performance by efficiently choosing features and also reduces the time complexity by 78%. The DenseNet-169 and VGGNet16 models extract the necessary properties to enhance remote sensing classification. Finally, validate the data by integrating additional validation data in the training set, which leads to more reliable and consistent accuracy and loss. The proposed VGGNet16 with Bi-LSTM and DenseNet169 with Bi-LSTM achieves accuracy on four distinct datasets 98.83% and 99.69% on UCM, 97.77% and 97.98% on AID, 95.06% and 96.84% on NWPU, whereas RSI-CB256 dataset achieved 98.5% and 98.91% accuracy<b>.</b> The hybrid fusion model distinguishes important from irrelevant data. To demonstrate its efficacy, authors conducted extensive tests on various distinct datasets such as UCM, AID, NWPU, RSICB256 and have ascertained that this approach exhibits superiority over existing state-of-the-art techniques currently employed in the field.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing remote sensing image analysis: optimization of a hybrid deep network through HHO algorithm

  • 期刊论文

  • 发表日期：

    2025年1月9日

查看全文
  • Monia Digra,
  • Renu Dhir,
  • Nonita Sharma

摘要

Remote sensing (RS) image interpretation includes multi-label picture classification, a key problem. The complicated and diverse character of many remote sensing scenes, which are produced by the spatial combination and association of various objects, makes classification difficult. In this research, an innovative approach that combines deep neural networks with bio-inspired optimization techniques to handle the challenges of classifying remote sensing images. First, the efficacy of proposed model was evaluated using the publicly accessible remote sensing scene dataset for multi-label classification. Second, this work employs the HHO (Harris Hawk optimization) model to improve classification performance by efficiently choosing features and also reduces the time complexity by 78%. The DenseNet-169 and VGGNet16 models extract the necessary properties to enhance remote sensing classification. Finally, validate the data by integrating additional validation data in the training set, which leads to more reliable and consistent accuracy and loss. The proposed VGGNet16 with Bi-LSTM and DenseNet169 with Bi-LSTM achieves accuracy on four distinct datasets 98.83% and 99.69% on UCM, 97.77% and 97.98% on AID, 95.06% and 96.84% on NWPU, whereas RSI-CB256 dataset achieved 98.5% and 98.91% accuracy. The hybrid fusion model distinguishes important from irrelevant data. To demonstrate its efficacy, authors conducted extensive tests on various distinct datasets such as UCM, AID, NWPU, RSICB256 and have ascertained that this approach exhibits superiority over existing state-of-the-art techniques currently employed in the field.