Name
Abstract | ||
|---|---|---|
There is a series of earth observation satellites called Landsat, which send a very large amount of image data every day such that it is hard to analyze manually. Thus an effective application of machine learning techniques to automatically analyze such data is called for. In surface object recognition, which is one of the important applications of such data, the distribution of a specific object on the surface is surveyed. In this paper, we propose and compare two methods for surface object recognition, one using the convolutional neural network (CNN) and the other support vector machine (SVM). In our experiments, CNN showed higher performance than SVM. In addition, we observed that the number of negative samples have a influence on the performance, and it is necessary to select the number of them for practical use. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MVA.2015.7153200 | 2015 14th IAPR International Conference on Machine Vision Applications (MVA) |
Keywords | Field | DocType |
CNN,SVM,Landsat 8 images,earth observation satellites,machine learning technique,surface object recognition,object distribution,convolutional neural network,support vector machine | Computer vision,Satellite,3D single-object recognition,Pattern recognition,Convolutional neural network,Computer science,Support vector machine,Artificial intelligence,Earth observation satellite,Cognitive neuroscience of visual object recognition | Conference |
Citations | PageRank | References |
3 | 0.55 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomohiro Ishii | 1 | 3 | 0.55 |
| Ryosuke Nakamura | 2 | 68 | 21.87 |
| Hidemoto Nakada | 3 | 956 | 118.87 |
| Yoshihiko Mochizuki | 4 | 8 | 2.38 |
| Hiroshi Ishikawa | 5 | 14 | 1.12 |