College of Electronic Science and Engineering, Jilin University, Changchun 130012, China.
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Comput Intell Neurosci. 2022 Jan 25;2022:8077563. doi: 10.1155/2022/8077563. eCollection 2022.
Aiming at the influence of different working conditions on recognition accuracy in remote sensing image recognition, this paper adopts hierarchical strategy to construct a network. Firstly, in order to establish the classification relationship between different samples, labeled samples are used for classification. A Logistic-T-distribution-Sparrow Search Algorithm-Least Squares Support Vector Machines (LOG-T-SSA-LSSVM) classification network is proposed. LOG-T-SSA algorithm is used to optimize parameters in LSSVM to establish a better network to achieve accurate classification between sample sets and then identify according to different categories. Through UCI dataset test, the accuracy of LOG-T-SSA-LSSVM network classification is significantly improved compared with that of contrast network. The autoencoder is integrated with Extreme Learning Machine, and the autoencoder is used to realize data compression. The advantages of Extreme Learning Machine (ELM) network, such as less training parameters, fast learning speed, and strong generalization ability, are fully utilized to realize efficient and supervised recognition. Experiments verify that the autoencoder-extreme learning machine (AE-ELM) network has a good recognition effect when the sigmoid activation function is selected and the number of hidden layer neurons are 2000. Finally, after image recognition under different working conditions, it is proved that the recognition accuracy of AE-ELM based on LOG-T-SSA-LSSVM classification is significantly improved compared with traditional ELM network and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) network.
针对遥感图像识别中不同工作条件对识别精度的影响，本文采用分层策略构建网络。首先，为了建立不同样本之间的分类关系，使用标记样本进行分类。提出了一种 LOG-T-SSA-LSSVM 分类网络。LOG-T-SSA 算法用于优化 LSSVM 中的参数，建立更好的网络，实现样本集之间的准确分类，然后根据不同类别进行识别。通过 UCI 数据集测试，与对比网络相比，LOG-T-SSA-LSSVM 网络分类的准确性显著提高。将自动编码器与极限学习机集成，利用自动编码器实现数据压缩，充分利用极限学习机（ELM）网络的训练参数少、学习速度快、泛化能力强等优点，实现高效有监督识别。实验验证了在选择 Sigmoid 激活函数和隐藏层神经元数为 2000 时，基于 LOG-T-SSA-LSSVM 分类的自动编码器-极限学习机（AE-ELM）网络具有良好的识别效果。最后，经过不同工作条件下的图像识别，证明了基于 LOG-T-SSA-LSSVM 分类的 AE-ELM 的识别精度明显高于传统的 ELM 网络和粒子群优化-极限学习机（PSO-ELM）网络。
