High resolution speckle interferometry by replacing temporal information with spatial information

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

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The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030166 ◽

2021 ◽

Vol 10 (3) ◽

pp. 166

Author(s):

Hartmut Müller ◽

Marije Louwsma

Keyword(s):

European Union ◽

Spatial Distribution ◽

Spatial Information ◽

National Level ◽

Temporal Information ◽

The European Union ◽

Member States ◽

Heavy Burden ◽

Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

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Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

Remote Sensing ◽

10.3390/rs13132473 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2473

Author(s):

Qinglie Yuan ◽

Helmi Zulhaidi Mohd Shafri ◽

Aidi Hizami Alias ◽

Shaiful Jahari Hashim

Keyword(s):

High Resolution ◽

Large Scale ◽

Spatial Information ◽

Feature Fusion ◽

Aerial Images ◽

Semantic Gap ◽

Superior Performance ◽

Lidar Data ◽

Building Extraction ◽

Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

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High-resolution near-infrared speckle interferometry and radiative transfer modeling of the OH/IR star OH 104.9+2.4

Astronomy and Astrophysics ◽

10.1051/0004-6361:20040352 ◽

2004 ◽

Vol 424 (1) ◽

pp. 165-177 ◽

Cited By ~ 6

Author(s):

D. Riechers ◽

Y. Balega ◽

T. Driebe ◽

K.-H. Hofmann ◽

A. B. Men'shchikov ◽

...

Keyword(s):

High Resolution ◽

Radiative Transfer ◽

Near Infrared ◽

Speckle Interferometry ◽

Radiative Transfer Modeling

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High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w4-197-2015 ◽

2015 ◽

Vol XL-7/W4 ◽

pp. 197-201 ◽

Cited By ~ 3

Author(s):

Y. Yang ◽

H. T. Li ◽

Y. S. Han ◽

H. Y. Gu

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Graph Theory ◽

High Resolution ◽

Spatial Information ◽

Remote Sensing Image ◽

Sensing Applications ◽

Initial Object ◽

Object Layer ◽

Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

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A time-stamp mechanism may provide temporal information necessary for egocentric to allocentric spatial transformations

eLife ◽

10.7554/elife.36769 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 16

Author(s):

Avner Wallach ◽

Erik Harvey-Girard ◽

James Jaeyoon Jun ◽

André Longtin ◽

Len Maler

Keyword(s):

Spatial Learning ◽

Spatial Organization ◽

Spatial Information ◽

Spatial Relations ◽

Temporal Information ◽

Accurate Estimation ◽

Neural Recordings ◽

Brain Circuitry ◽

Learning Circuits ◽

Thalamic Region

Learning the spatial organization of the environment is essential for most animals’ survival. This requires the animal to derive allocentric spatial information from egocentric sensory and motor experience. The neural mechanisms underlying this transformation are mostly unknown. We addressed this problem in electric fish, which can precisely navigate in complete darkness and whose brain circuitry is relatively simple. We conducted the first neural recordings in the preglomerular complex, the thalamic region exclusively connecting the optic tectum with the spatial learning circuits in the dorsolateral pallium. While tectal topographic information was mostly eliminated in preglomerular neurons, the time-intervals between object encounters were precisely encoded. We show that this reliable temporal information, combined with a speed signal, can permit accurate estimation of the distance between encounters, a necessary component of path-integration that enables computing allocentric spatial relations. Our results suggest that similar mechanisms are involved in sequential spatial learning in all vertebrates.

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A two-stream network with joint spatial-temporal distance for video-based person re-identification

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-192067 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3769-3781

Author(s):

Zhisong Han ◽

Yaling Liang ◽

Zengqun Chen ◽

Zhiheng Zhou

Keyword(s):

Spatial Information ◽

Temporal Information ◽

Temporal Distance ◽

Spatial Distance ◽

Weighted Sum ◽

Stream Network ◽

Spatial Features ◽

Public Datasets ◽

Types Of Information ◽

Temporal Information Extraction

Video-based person re-identification aims to match videos of pedestrians captured by non-overlapping cameras. Video provides spatial information and temporal information. However, most existing methods do not combine these two types of information well and ignore that they are of different importance in most cases. To address the above issues, we propose a two-stream network with a joint distance metric for measuring the similarity of two videos. The proposed two-stream network has several appealing properties. First, the spatial stream focuses on multiple parts of a person and outputs robust local spatial features. Second, a lightweight and effective temporal information extraction block is introduced in video-based person re-identification. In the inference stage, the distance of two videos is measured by the weighted sum of spatial distance and temporal distance. We conduct extensive experiments on four public datasets, i.e., MARS, PRID2011, iLIDS-VID and DukeMTMC-VideoReID to show that our proposed approach outperforms existing methods in video-based person re-ID.

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Mapping Heat Stress Vulnerability and Risk Assessment at the Neighborhood Scale to Drive Urban Adaptation Planning

Sustainability ◽

10.3390/su12031056 ◽

2020 ◽

Vol 12 (3) ◽

pp. 1056 ◽

Cited By ~ 2

Author(s):

Denis Maragno ◽

Michele Dalla Fontana ◽

Francesco Musco

Keyword(s):

Risk Assessment ◽

Heat Stress ◽

High Resolution ◽

Planning Process ◽

Spatial Information ◽

Local Level ◽

Adaptation Planning ◽

Stress Vulnerability ◽

Neighborhood Scale ◽

The City

Climate change is one of the most complex issues of the 21st century, and even though there is general consensus about the urgency of taking action at the city level, the planning and implementation of adaptation measures is advancing slowly. The lack of data and information to support the planning process is often mentioned as a factor hampering the adaptation processes in cities. In this paper, we developed and tested a methodology for heat stress vulnerability and risk assessment at the neighborhood scale to support designers, planners, and decision makers in developing and implementing adaptation strategies and measures at the local level. The methodology combines high-resolution spatial information and crowdsourcing geospatial data to develop sensitivity, adaptive capacity, vulnerability, exposure, and risk indicators. The methodology is then tested on the urban fabric of the city of Padova, Italy. Our results show that different vulnerability and risk values correspond to different typologies of urban areas. Furthermore, the possibility of combining high-resolution information provided by the indicators and land use categories is of great importance to support the adaptation planning process. We also argue that the methodology is flexible enough to be applied in different contexts.

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Speckle Interferometry. III. High-Resolution Measurements of Twelve Close Binary Systems

The Astrophysical Journal ◽

10.1086/181689 ◽

1974 ◽

Vol 194 ◽

pp. L147 ◽

Cited By ~ 42

Author(s):

A. Labeyrie ◽

D. Bonneau ◽

R. V. Stachnik ◽

D. Y. Gezari

Keyword(s):

High Resolution ◽

Binary Systems ◽

Speckle Interferometry ◽

Close Binary ◽

Close Binary Systems

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Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization

Applied Sciences ◽

10.3390/app9204444 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4444

Author(s):

Byunghyun Kim ◽

Soojin Cho

Keyword(s):

High Resolution ◽

Spatial Information ◽

Super Resolution ◽

Spatial Mismatch ◽

Low Resolution ◽

Practical Applications ◽

Histogram Matching ◽

Shear Transformation ◽

Entire Dataset ◽

Rgb Image

In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.

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