Name
Abstract | ||
|---|---|---|
We address the problem of multi-label scene classification from Very High Resolution (VHR) satellite remote sensing (RS) images in this paper by exploring the deep graph convolutional network (GCN). Since a given VHR RS scene contains several local features, the traditional single-label classification frameworks do not convey the true semantics of the scene. The multi-label classification approaches, on the other hand, is expected to aid in better characterization of the area under consideration. Under the multi-label setup, we find it intuitive to represent a given image as a region adjacency graph (RAG) of the respective local regions. In order to extract discriminative features from such irregular structures for enhanced classification, we model the subsequent supervised learning problem in terms of a novel multi-label deep GCN. We validate the efficacy of the proposed technique on the popular UC-Merced dataset where our framework outperforms with respect to the literature. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2019.05.024 | Neurocomputing |
Keywords | Field | DocType |
Graph convolutional network,Multi-label classification,Very high resolution images,Remote sensing | Adjacency list,Graph,Pattern recognition,Satellite remote sensing,Supervised learning,Artificial intelligence,Discriminative model,Mathematics,Semantics | Journal |
Volume | ISSN | Citations |
357 | 0925-2312 | 1 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nagma Khan | 1 | 1 | 0.34 |
| Ushasi Chaudhuri | 2 | 11 | 4.23 |
| Biplab Banerjee | 3 | 57 | 23.15 |
| Subhasis Chaudhuri | 4 | 1384 | 133.18 |