Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks

Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.info:eu-repo/semantics/publishedVersio

Electronic properties of semiconductor quantum dots. A scanning tunneling microscopy and spectroscopy study.

Item does not contain fulltextRadboud University Nijmegen, Scanning Probe Microscopy, Scanning Probe Microscopy, 10 november 2008Promotores : Speller, p.d.D.V. promotores: pro, Vanmaekelbergh, D.A.M.113 p

Size-dependent single-particle energy levels and interparticle Coulomb interactions in CdSe quantum dots measured by scanning tunneling spectroscopy

Contains fulltext : 35402.pdf (publisher's version ) (Open Access

Can scanning tunnelling spectroscopy measure the density of states of semiconductor quantum dots?

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Towards spin-polarized scanning tunneling microscopy on magnetite (110)

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Nano-magnetic probing on magnetite (110)

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Linewidth of resonances in scanning tunneling spectroscopy

Cryogenic scanning tunneling spectroscopy is increasingly used to study the electronic structure of adatoms, molecules, and semiconductor quantum dots. However, the width of the conductance resonances that indicate the energy levels is much larger than the thermal energy, and this is not well understood. Here, we present a comprehensive study of the line shape and width of the conductance resonances observed with small colloidal semiconductor quantum dots. Experimentally, the line shape and width are studied for CdSe quantum dots of different sizes, with nanocrystals being chemically or physically attached to the substrate. The influence of the temperature is studied from 5 K up to room temperature. We have also varied the set-point current via the tip-to-dot distance to study the effects of dissipative heating of the quantum dot. We present basic calculations of the effects of electron-phonon coupling, charge and dipole fluctuations in the close environment of the quantum dot, mechanical oscillations of the quantum dot in the tunneling junction, and internal heating by nonresonant electron transport. A comparison with the experimental results shows that electron-phonon coupling forms the main contribution to the line broadening for the lowest resonance. Fluctuations of the charge landscape around the quantum dot are most likely involved in an additional broadening. More importantly, these potential fluctuations wash out the vibronic structure of the line shape that should arise from electron-phonon coupling. Our results show that in the case of semiconductor quantum dots, internal heating due to nonresonant electron transport is not important