Name
Abstract | ||
|---|---|---|
In this paper we address the problem of urban optical imagery classification by developing a convolutional neural network (CNN) approach. We design a custom CNN that operates on local patches in order to produce dense pixel-level classification map. In this work we focus on a comprehensive dataset of 2.5-meter SPOT-5 imagery acquired at different dates and sites. The performance of the proposed model is validated on a five target-class problem and compared with a benchmark random forest classifier with a set of hand-picked features. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729461 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Convolutional neural networks, classification, optical imagery, large urban zones | Computer vision,Computer science,Convolutional neural network,Remote sensing,Artificial intelligence,Random forest,Image resolution,Benchmark (computing),Machine learning | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.63 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir A. Krylov | 1 | 133 | 14.81 |
| Michaela De Martino | 2 | 14 | 4.28 |
| Gabriele Moser | 3 | 919 | 76.92 |
| Sebastiano B. Serpico | 4 | 749 | 64.86 |