Name
Title | ||
|---|---|---|
Semi-Supervised Cross-View Scene Model Adaptation For Remote Sensing Image Classification |
Abstract | ||
|---|---|---|
In this paper, we address the problem of semi-supervised visual domain adaptation for transferring scene category models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a multiple kernel learning domain adaptation algorithm to fuse the information from multiple features and cope with the considerable variation in feature distributions between images from two domains. For each image, we first extract eight state-of-art local features and use the pretrained scene attribute model from ground-level SUN attribute database to predict attribute labels. For each scene class we learn an adapted target classifier based on multiple feature kernels by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Experimental results demonstrate that it is possible to use a scene category model learned on a set of ground view scenes for semi-supervised classification of VHR remote sensing images. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729613 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Remote sensing image classification, scene attributes, domain adaptation, cross-view | Computer science,Remote sensing,Artificial intelligence,Classifier (linguistics),Fuse (electrical),Contextual image classification,Kernel (linear algebra),Computer vision,Pattern recognition,Visualization,Support vector machine,Multiple kernel learning,Feature extraction | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhipeng Deng | 1 | 4 | 1.74 |
| Hao Sun | 2 | 56 | 7.07 |
| Shilin Zhou | 3 | 72 | 13.94 |
| Kefeng Ji | 4 | 176 | 17.01 |