Name
Abstract | ||
|---|---|---|
Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In the past, many different engineered features have been proposed for the HSI classification problem. In this paper, however, we propose a feature learning approach for hyperspectral image classification based on convolutional neural networks (CNNs). The proposed CNN model is able to learn structured features, roughly resembling different spectral band-pass filters, directly from the hyperspectral input data. Our experimental results, conducted on a commonly-used remote sensing hyperspectral dataset, show that the proposed method provides classification results that are among the state-of-the-art, without using any prior knowledge or engineered features. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2733373.2806306 | ACM Multimedia |
Keywords | Field | DocType |
Classification,convolutional neural networks,deep learning,hyperspectral imaging | Hyperspectral image classification,Computer vision,Scene analysis,Pattern recognition,Convolutional neural network,Computer science,Hyperspectral imaging,Artificial intelligence,Deep learning,Feature learning | Conference |
Citations | PageRank | References |
7 | 0.60 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viktor Slavkovikj | 1 | 79 | 5.41 |
| Steven Verstockt | 2 | 55 | 13.58 |
| Wesley De Neve | 3 | 525 | 54.41 |
| Sofie Van Hoecke | 4 | 113 | 26.27 |
| Rik Van de Walle | 5 | 2040 | 238.28 |