Name
Title | ||
|---|---|---|
Gravitational Search Optimized Hyperspectral Image Classification with Multilayer Perceptron. |
Abstract | ||
|---|---|---|
Hyperspectral image classification has been widely used in a variety of applications such as land cover analysis, mining, change detection and disaster evaluation. As one of the most-widely used classifiers, the Multilayer Perception (MLP) has shown impressive classification performance. However, the MLP is very sensitive to the setting of the training parameters such as weights and biases. The traditional parameter training methods, such as, error back propagation algorithm (BP), are easily trapped into local optima and suffer premature convergence. To address these problems, this paper introduces a modified gravitational search algorithm (MGSA) by employing a multi-population strategy to let four sub-populations explore the different areas in search space and a Gaussian mutation operator to mutate the global best individual when swarm stagnate. After that, MGSA is used to optimize the weights and biases of MLP. The experimental results on a public dataset have validated the higher classification accuracy of the proposed method. |
Year | Venue | Field |
|---|---|---|
2018 | BICS | Change detection,Swarm behaviour,Pattern recognition,Premature convergence,Local optimum,Computer science,Multilayer perceptron,Operator (computer programming),Artificial intelligence,Backpropagation,Land cover |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
11 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Ma | 1 | 33 | 4.85 |
| Aizhu Zhang | 2 | 62 | 9.98 |
| Genyun Sun | 3 | 149 | 17.27 |
| Xuming Zhang | 4 | 1 | 1.39 |
| Jun Rong | 5 | 3 | 1.39 |
| hui huang | 6 | 84 | 17.04 |
| Yanling Hao | 7 | 0 | 0.68 |
| Xueqian Rong | 8 | 0 | 1.01 |
| Hongzhang Ma | 9 | 0 | 0.34 |