Ding, Peng ; Zhang, Ye ; Jia, Ping ; Chang, Xu-ling (2019)
A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images.
In: Neural Processing Letters, 49 (3)
doi: 10.1007/s11063-018-9878-5
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
In recent years, deep learning especially deep convolutional neural networks (DCNN) has made great progress. Many researchers take advantage of different DCNN models to do object detection in remote sensing. Different DCNN models have different advantages and disadvantages. But in the field of remote sensing, many scholars usually do comparison between DCNN models and traditional machine learning. In this paper, we compare different state-of-the-art DCNN models mainly over two publicly available remote sensing datasets-airplane dataset and car dataset. Such comparison can provide guidance for related researchers. Besides,we provide suggestions for fine-tuning different DCNN models.Moreover, forDCNNmodels including fully connected layers,we provide amethod to save storage space..
| Typ des Eintrags: | Artikel | ||||
|---|---|---|---|---|---|
| Erschienen: | 2019 | ||||
| Autor(en): | Ding, Peng ; Zhang, Ye ; Jia, Ping ; Chang, Xu-ling | ||||
| Art des Eintrags: | Bibliographie | ||||
| Titel: | A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images | ||||
| Sprache: | Englisch | ||||
| Publikationsjahr: | 2019 | ||||
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Neural Processing Letters | ||||
| Jahrgang/Volume einer Zeitschrift: | 49 | ||||
| (Heft-)Nummer: | 3 | ||||
| DOI: | 10.1007/s11063-018-9878-5 | ||||
| URL / URN: | https://doi.org/10.1007/s11063-018-9878-5 | ||||
| Kurzbeschreibung (Abstract): | In recent years, deep learning especially deep convolutional neural networks (DCNN) has made great progress. Many researchers take advantage of different DCNN models to do object detection in remote sensing. Different DCNN models have different advantages and disadvantages. But in the field of remote sensing, many scholars usually do comparison between DCNN models and traditional machine learning. In this paper, we compare different state-of-the-art DCNN models mainly over two publicly available remote sensing datasets-airplane dataset and car dataset. Such comparison can provide guidance for related researchers. Besides,we provide suggestions for fine-tuning different DCNN models.Moreover, forDCNNmodels including fully connected layers,we provide amethod to save storage space.. |
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| Freie Schlagworte: | Object detection, Neural networks, Deep learning | ||||
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| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
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| Hinterlegungsdatum: | 19 Jun 2019 06:56 | ||||
| Letzte Änderung: | 19 Jun 2019 06:56 | ||||
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| Suche nach Titel in: | TUfind oder in Google | ||||
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