Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 11-18 |
| Seitenumfang | 8 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 3 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 2 Juni 2016 |
| Veranstaltung | 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016 - Prague, Tschechische Republik Dauer: 12 Juli 2016 → 19 Juli 2016 |
Abstract
In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Instrumentierung
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in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 3, Nr. 3, 02.06.2016, S. 11-18.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK
AU - Chen, L.
AU - Rottensteiner, F.
AU - Heipke, C.
N1 - Funding Information: The author Lin Chen would like to thank the China Scholarship Council (CSC) for financially supporting his PhD study at Leibniz Universität Hannover, Germany. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2016/6/2
Y1 - 2016/6/2
N2 - In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
AB - In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
KW - CNN
KW - Descriptor Learning
KW - Nesterov's Gradient Descent
KW - Patch Comparison
KW - Siamese Architecture
UR - http://www.scopus.com/inward/record.url?scp=85042522425&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-III-3-11-2016
DO - 10.5194/isprs-annals-III-3-11-2016
M3 - Conference article
AN - SCOPUS:85042522425
VL - 3
SP - 11
EP - 18
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 3
T2 - 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016
Y2 - 12 July 2016 through 19 July 2016
ER -