Remote sensing image classification method based on membrane calculation, PSO particle swarm and BP neural network
By combining membrane calculation, PSO particle swarm algorithm and BP neural network in remote sensing image classification, the parameters of BP neural network are optimized, and the problems of slow classification speed, low accuracy and easy to fall into local optimality in the existing technology are solved, achieving a more efficient and accurate classification effect.
CN119992164APending Publication Date: 2025-05-13XIAN SPACE STAR TECH IND GRP
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Patent Information
- Application Number
- CN202411987226.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-05-13
AI Technical Summary
Technical Problem
The existing remote sensing image classification methods are slow in processing big data, have low accuracy, and are prone to falling into local optimal solutions, affecting the classification effect.
Method used
Using the method based on membrane computing + PSO particle swarm + BP neural network, the PSO particle swarm optimization algorithm is fused through the membrane computing framework to find the global optimal solution to optimize the classification detection model of the BP neural network, thereby achieving rapid classification of remote sensing images.
Benefits of technology
It improves the efficiency and accuracy of remote sensing image classification, reduces network training time, and effectively avoids the problem of local optimal solutions.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention belongs to the technical field of remote sensing data intelligent classification, and discloses a remote sensing image classification method based on membrane calculation, PSO particle swarm and BP neural network, which uses an organization type P system in membrane calculation as a calculation framework, and uses a speed-displacement model of the PSO particle swarm algorithm as an evolution rule of an object in a basic membrane. Meanwhile, sharing and co-evolution of information between objects are achieved through a transfer rule of a membrane, finally, a global optimal object is stored in the environment, then the global optimal object obtained in the environment serves as input of the optimized BP neural network, finally, the BP neural network is trained, and the optimal object is obtained. And inputting remote sensing image data into the trained BP neural network to obtain a final remote sensing image classification result. The remote sensing image classification efficiency and precision are improved, and rapid classification of the remote sensing images is realized.
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