Name
Abstract | ||
|---|---|---|
Hyperspectral sensing, due to its intrinsic ability to capture the spectral responses of depicted materials, provides unique capabilities towards object detection and identification. In this paper, we tackle the problem of man-made object detection from hyperspectral data through a deep learning classification framework. By the effective exploitation of a Convolutional Neural Network we encode pixels' spectral and spatial information and employ a Multi-Layer Perceptron to conduct the classification task. Experimental results and the performed quantitative validation on widely used hyperspectral datasets demonstrating the great potentials of the developed approach towards accurate and automated man-made object detection. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-27857-5_64 | ADVANCES IN VISUAL COMPUTING, PT I (ISVC 2015) |
Field | DocType | Volume |
Spatial analysis,Computer vision,Object detection,Pattern recognition,Computer science,Convolutional neural network,Deep belief network,Hyperspectral imaging,Artificial intelligence,Pixel,Deep learning,Perceptron | Conference | 9474 |
ISSN | Citations | PageRank |
0302-9743 | 7 | 0.48 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Makantasis | 1 | 108 | 13.94 |
| Konstantinos Karantzalos | 2 | 173 | 18.11 |
| Anastasios D. Doulamis | 3 | 883 | 93.64 |
| Konstantinos Loupos | 4 | 17 | 2.39 |