Abstract
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.
Highlights
As the basis of computer vision application, object detection is one of the most widely concerned problems in real life
After image recognition under different working conditions, it is proved that the recognition accuracy of autoencoder-extreme learning machine (AE-ELM) based on LOG-T-Sparrow Search Algorithm (SSA)-Least Squares Support Vector Machine (LSSVM) classification is significantly improved compared with traditional ELM network and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) network
A hierarchical strategy is used to construct a network for remote sensing image recognition
Summary
As the basis of computer vision application, object detection is one of the most widely concerned problems in real life. Existing target detection methods can be divided into two categories: methods based on manual feature construction and methods based on deep learning. As deep learningbased methods have made great achievements in the field of target detection, related extended methods have been applied to remote sensing images. Deep learning-based target detection methods can generally be divided into two categories: region proposal-based methods, namely, twostage detection, and regression-based methods, represented as one-stage detection. R-CNN and its variant methods have been successfully applied in the field of remote sensing image detection, it is undeniable that the training process is very clumsy and slow. LOG-T-SSA algorithm is used to optimize parameters in LSSVM to establish a better network to achieve accurate classification between sample sets and identify according to different categories. E third part introduces AE-ELM network recognizer. e fourth part constructs the recognizer combining LOG-T-SSA-LSSVM and AE-ELM. e fifth part carries on the relevant experiment verification. e last part is the conclusion and future development
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