Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have resurged lately due to their state-of-the-art performance in various disciplines, such as computer vision, audio and text processing. However, CNNs have not been widely employed for remote sensing applications. In this paper, we propose a CNN architecture, named Modular-CNN, to improve the performance of building detectors that employ Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) in a remote sensing dataset. Additionally, we propose two improvements to increase the classification accuracy of Modular-CNN. The first improvement combines the power of raw and normalised features, while the second one concerns the Euler transformation of feature vectors. We demonstrate the effectiveness of our proposed Modular-CNN and the novel improvements in remote sensing and other datasets in a comparative study with other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.comnet.2019.107034 | Computer Networks |
Keywords | Field | DocType |
Remote sensing,Modular-CNN,Building detection | Feature vector,Computer science,Convolutional neural network,Remote sensing,Local binary patterns,Remote sensing application,Histogram of oriented gradients,Modular design,Detector,Text processing | Journal |
Volume | ISSN | Citations |
168 | 1389-1286 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitrios Konstantinidis | 1 | 0 | 0.34 |
| Vasileios Argyriou | 2 | 279 | 30.51 |
| Tania Stathaki | 3 | 359 | 37.45 |
| Grammalidis, N. | 4 | 9 | 1.68 |