Name
Title | ||
|---|---|---|
Semi-Supervised Ground-to-Aerial Adaptation with Heterogeneous Features Learning for Scene Classification. |
Abstract | ||
|---|---|---|
Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotations have been collected in the vision community. In this paper, we attempt to exploit the abundant labeled ground-level images to build discriminative models for overhead-view RSI classification. However, images from the ground-level and overhead view are represented by heterogeneous features with different distributions; how to effectively combine multiple features and reduce the mismatch of distributions are two key problems in this scene-model transfer task. Specifically, a semi-supervised manifold-regularized multiple-kernel-learning (SMRMKL) algorithm is proposed for solving these problems. We employ multiple kernels over several features to learn an optimal combined model automatically. Multi-kernel Maximum Mean Discrepancy (MK-MMD) is utilized to measure the data mismatch. To make use of unlabeled target samples, a manifold regularized semi-supervised learning process is incorporated into our framework. Extensive experimental results on both cross-view and aerial-to-satellite scene datasets demonstrate that: (1) SMRMKL has an appealing extension ability to effectively fuse different types of visual features; and (2) manifold regularization can improve the adaptation performance by utilizing unlabeled target samples. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/ijgi7050182 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
remote sensing,scene classification,heterogeneous domain adaptation,cross-view,multiple kernel learning | Maximum mean discrepancy,Pattern recognition,Computer science,Multiple kernel learning,Manifold regularization,Exploit,Artificial intelligence,Fuse (electrical),Discriminative model,Manifold | Journal |
Volume | Issue | Citations |
7 | 5 | 1 |
PageRank | References | Authors |
0.36 | 26 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhipeng Deng | 1 | 4 | 1.74 |
| Hao Sun | 2 | 56 | 7.07 |
| Shilin Zhou | 3 | 72 | 13.94 |