Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

MOD(i) : a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra

MOD(i) () is a powerful and convenient web service that facilitates the interpretation of tandem mass spectra for identifying post-translational modifications (PTMs) in a peptide. It is powerful in that it can interpret a tandem mass spectrum even when hundreds of modification types are considered and the number of potential PTMs in a peptide is large, in contrast to most of the methods currently available for spectra interpretation that limit the number of PTM sites and types being used for PTM analysis. For example, using MOD(i), one can consider for analysis both the entire PTM list published on the unimod webpage () and user-defined PTMs simultaneously, and one can also identify multiple PTM sites in a spectrum. MOD(i) is convenient in that it can take various input file formats such as .mzXML, .dta, .pkl and .mgf files, and it is equipped with a graphical tool called MassPective developed to display MOD(i)'s output in a user-friendly manner and helps users understand MOD(i)'s output quickly. In addition, one can perform manual de novo sequencing using MassPective

적대적 공격을 이용한 항공 이미지 분할에 대한 도메인 적응

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).YⅠ. Introduction 1 1.1. Semantic segmentation of aerial images 1 1.2. Challenging issue in CNN-based segmentation models 2 1.3. Overview of proposed scheme 2 Ⅱ. Basic architecture for semantic segmentation 4 2.1. Semantic segmentation systems 4 2.2. Inria aerial image labeling dataset 6 2.3. Training setup 6 2.4. Test results and comparison with other architectures 7 2.5. Test results with other datasets 8 2.6. Related work for domain adaptation 11 Ⅲ. Domain adaptive transfer attack (DATA) 11 3.1. Overview of the proposed model 12 3.2. Objective function for the generator 14 3.3. Objective function for discriminator 15 3.4. Training the adversarial attack model & discriminator 16 Ⅳ. DATA-based adversarial training and results 19 4.1. Adversary training setup 19 4.2. Comparison with Other Methods 21 4.3. Extended experiments in various environments 24 Ⅴ. Conclusion 26 References 28센서 기술의 발달로 다양한 원격 감지 이미지 사용이 가능해졌다. 항공 이미지를 활용하기 위해서는 관심 대상을 추출해서 분할해야 한다. 최근에는 컨볼루션 신경망 (CNN)을 기반으로 한 알고리즘이 활발히 연구되고 있고 항공 이미지 분할에서 뛰어난 성능을 보여준다. 하지만 제한된 일반화 능력에 대한 문제가 여전히 존재한다. 학습 데이터가 목표 데이터와 유사하지 않다면 딥러닝 모델은 목표 데이터세트에서 객체를 분할하지 못한다. 본 논문은 제한된 일반화 문제를 해결하기 위해 적대적 공격 기반 도메인 적응 방법 (Domain Adaptive Transfer Attack, DATA)을 제안한다. 제안된 시스템은 도메인 이전과 적대적 공격 개념을 결합한다. DATA 계획법은 입력 이미지의 분포를 목적 이미지의 분포로 이동시키면서 이미지를 분할 네트워크에 대한 적대적인 예제로 전환할 수 있다. 목적 도메인 쪽으로 옮겨진 적대적인 예제를 방어함으로써 분할 네트워크는 도메인 차이로 인한 성능 저하를 극복한다. 제안한 기법은 세 가지 데이터세트 (Inria aerial image la-beling dataset, Massachusetts building dataset, WHU East Asia dataset)에 대한 교차 실험에서 그 효과를 입증했다.MasterdCollectio

Effects of environmental parameters for offshore wind turbine system with jacket support structure

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Three-dimensional artificial chirality towards low-cost and ultra-sensitive enantioselective sensing

Artificial chiral structures have potential applications in the field of enantioselective signal sensing. Advanced nanofabrication methods enable a large diversity in geometric structures and broad selectivity of materials, which can be exploited to manufacture artificial three-dimensional chiral structures. Various chiroptical phenomena exploiting spin and orbital angular momentum at the nanoscale have been continuously exploited as a way to effectively detect enantiomers. This review introduces precisely controlled bottom-up and large-area top-down metamaterial fabrication methods to solve the limitations of high manufacturing cost and low production speed. Particle synthesis, self-assembly, glanced angled vapor deposition, and three-dimensional plasmonic nanostructure printing are introduced. Furthermore, emerging sensitive chiral sensing methods such as cavity-enhanced chirality, photothermal circular dichroism, and helical dichroism of single particles are discussed. The continuous progress of nanofabrication technology presents the strong potential for developing artificial chiral structures for applications in biomedical, pharmaceutical, nanophotonic systems.11Nsciescopu

Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images

Change detection is an important task in the field of remote sensing. Various change detection methods based on convolutional neural networks (CNNs) have recently been proposed for remote sensing using satellite or aerial images. However, existing methods allow only the partial use of content information in images during change detection because they adopt simple feature-similarity measurements or pixel-level loss functions to construct their network architectures. Therefore, when these methods are applied to complex urban areas, their performance in terms of change detection tends to be limited. In this paper, a novel CNN-based change detection approach, referred to as a local similarity Siamese network (LSS-Net), with a cosine similarity measurement, has been proposed for better urban land change detection in remote sensing images. To use content information on two sequential images, a new change attention map-based content loss (CAC loss) function was developed in this study. In addition, to enhance the performance of LSS-Net in terms of change detection, a suitable feature-similarity measurement method, incorporated into a local similarity attention module, was determined through systemic experiments. To verify the change detection performance of LSS-Net, it was compared with other state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of F1 score (0.9630, 0.9377, and 0.7751), and kappa (0.9581, 0.9351, and 0.7646) on the three test datasets, thus suggesting its potential for various remote sensing applications. CCBYTRU

Facile fabrication of high-aspect-ratio metasurfaces by modulation of surface tensions in nanoparticle-embedded-resin printing

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