提出新型网络模型，提升遥感图像场景分类准确率至97%。
Utilizing a Novel Deep Learning Method for Scene Categorization in Remote Sensing Data
- 用鱿鱼优化的双向循环神经网络捕捉遥感图像特征。
- 在多个数据集上达到97%准确率，优于现有方法。
- 适合遥感图像分析、灾害监测等需要高精度的应用。
遥感图像中的场景分类（SC）在灾害防控、生态观测、城市规划等领域具有广泛应用，但实现高精度分类仍具挑战。传统深度学习模型依赖大规模、高噪声、多样化数据以提取关键视觉特征。为此，本文提出一种新方法——鱿鱼优化双向循环神经网络（CO-BRNN），用于遥感数据中的场景分类。实验对比了CO-BRNN与MLP-CNN、CNN-LSTM、LSTM-CRF、GB、MIRM-CF、CNN-DA等主流方法。结果表明，CO-BRNN达到最高准确率97%，其次为LSTM-CRF的90%、MLP-CNN的85%和CNN-LSTM的80%。研究强调了物理验证对卫星数据效率的重要性。
原文摘要 · Abstract (English)
Scene categorization (SC) in remotely acquired images is an important subject with broad consequences in different fields, including catastrophe control, ecological observation, architecture for cities, and more. Nevertheless, its several apps, reaching a high degree of accuracy in SC from distant observation data has demonstrated to be difficult. This is because traditional conventional deep learning models require large databases with high variety and high levels of noise to capture important visual features. To address these problems, this investigation file introduces an innovative technique referred to as the Cuttlefish Optimized Bidirectional Recurrent Neural Network (CO- BRNN) for type of scenes in remote sensing data. The investigation compares the execution of CO-BRNN with current techniques, including Multilayer Perceptron- Convolutional Neural Network (MLP-CNN), Convolutional Neural Network-Long Short Term Memory (CNN-LSTM), and Long Short Term Memory-Conditional Random Field (LSTM-CRF), Graph-Based (GB), Multilabel Image Retrieval Model (MIRM-CF), Convolutional Neural Networks Data Augmentation (CNN-DA). The results demonstrate that CO-BRNN attained the maximum accuracy of 97%, followed by LSTM-CRF with 90%, MLP-CNN with 85%, and CNN-LSTM with 80%. The study highlights the significance of physical confirmation to ensure the efficiency of satellite data.
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