Mapping of landscape-morphological structure of the Northern-Western part of Anapa bay bar by digital high resolution aerial images

Anapa bay bar is a valuable recreational-medical resource. Digital landscape-morphological mapping of its the Northern-Western part was created by digital aero survey materials for monitoring of its statement. Compiled maps show that in the Western part of region dune belt is degradated, front dune hills destroyed due to spreading of settlement Veselovka buildings to beach, and due to mass enactments carrying out at bay bar of lake Solenoe. Here it is necessary to decide the problem of defense from waves flooding by construction of artificial hills. The middle part of region, around Bugaz lagoon, is using for unregulated recreation of extreme sportsmen – windsurfing and kiting – with seasonal recreation in camping from tent-city and campers. Many short roads to sea beach, orthogonal to coast line, have been transformed to corridors of blowing and sea waves interaction to lagoon lowland with dune belt destroying. In the Eastern part of region, at Bugaz bay bar, dune belt is conserve, it changes under natural sea and wind processes action. At some places sea waves are erode windward front dune slope. Just everywhere sand accumulative trains are forming at leeward slope of front dune. Showed peculiarities of landscape morphological structure mast be taken in account due treatment of measures for bay bar defense and keeping.

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Mapping of landscape-morphological structure of the Northern-Western part of Anapa bay bar by digital high resolution aerial images

Geodesy and Cartography ◽

10.22389/0016-7126-2017-929-11-29-39 ◽

2017 ◽

Vol 929 (11) ◽

pp. 29-39

Author(s):

V.I. Kravtsovа ◽

E.R. Chalova

Keyword(s):

Morphological Structure ◽

Aerial Survey ◽

Middle Part ◽

Aerial Images ◽

Sea Waves ◽

Public Events ◽

The North ◽

Waves Interaction ◽

Account Due ◽

Digital Landscape

The Anapa bay bar is a valuable recreational and medical resource. Digital landscape and morphological mapping of its the North-Western part was created by digital aerial survey materials for monitoring its statement. The compiled maps show that in the Western part of region dune belt is degraded; the front dune hills were destroyed due to spreading the settlement of Veselovka buildings to the beach, and due to mass enactments carrying out at the bay bar of lake Solenoye. Here it is necessary to solve the problem of defense from waves flooding by constructing artificial hills. The middle part of the region, around the Bugaz lagoon, is used for unregulated extremal sports such as windsurfing and kiting, seasonal recreation in tent-camping and public events. Many short roads to the sea beach, orthogonal to the coast line, have turned into corridors of blowing and sea waves’ interaction with lagoon lowland destroying the dune belt. In the Eastern part of the region, at the Bugaz bay bar, the dune belt is conserved, but it changes under natural sea and wind processes action. In some places sea waves erode the windward front dune slope. Just everywhere sand accumulative trains are formed at the leeward slope of the front dune. It is shown that peculiarities of landscape morphological structure mast be taken in account due to taking measures for the bay bar protecting and preservation.

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High-resolution topographic SEM and radiation damage: The rationale for using low beam voltage

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100131024 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1278-1279

Author(s):

James Pawley ◽

David Joy

Keyword(s):

High Resolution ◽

Imaging Techniques ◽

Morphological Structure ◽

Radiation Sensitivity ◽

Beam Specimen ◽

Measured Signal ◽

Practical Consideration ◽

Interaction Volume ◽

Impact Area ◽

Signal Contrast

The scanning electron microscope (SEM) builds up an image by sampling contiguous sub-volumes near the surface of the specimen. A fine electron beam selectively excites each sub-volume and then the intensity of some resulting signal is measured and then plotted as a corresponding intensity in an image. The spatial resolution of such an image is limited by at least three factors. Two of these determine the size of the interaction volume: the size of the electron probe and the extent to which detectable signal is excited from locations remote from the beam impact area. A third limitation emerges from the fact that the probing beam is composed of a number of discrete particles and therefore that the accuracy with which any detectable signal can be measured is limited by Poisson statistics applied to this number (or to the number of events actually detected if this is smaller). As in all imaging techniques, the limiting signal contrast required to recognize a morphological structure is constrained by this statistical consideration. The only way to overcome this limit is to increase either the contrast of the measured signal or the number of beam/specimen interactions detected. Unfortunately, these interactions deposit ionizing radiation that may damage the very structure under investigation. As a result, any practical consideration of the high resolution performance of the SEM must consider not only the size of the interaction volume but also the contrast available from the signal producing the image and the radiation sensitivity of the specimen.

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Classification Techniques Of High Resolution Aerial Images: A Survey Of Commonly A.I Techniques

10.36458/1253-000-032-014 ◽

2018 ◽

pp. 304

Author(s):

نداء فليح حسن ◽

علي صبار محسن

Keyword(s):

High Resolution ◽

Aerial Images ◽

Classification Techniques

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Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: A hybrid approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191547 ◽

2020 ◽

Vol 39 (1) ◽

pp. 543-560

Author(s):

Muthukumaran Malarvel ◽

Soumya Ranjan Nayak

Keyword(s):

High Resolution ◽

Density Estimation ◽

Kernel Density Estimation ◽

Hybrid Approach ◽

Kernel Density ◽

Aerial Images ◽

Region Segmentation

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Dynamics of Erosion and Deposition in a Partially Restored Valley-Bottom Gully

Land ◽

10.3390/land10010062 ◽

2021 ◽

Vol 10 (1) ◽

pp. 62

Author(s):

Alberto Alfonso-Torreño ◽

Álvaro Gómez-Gutiérrez ◽

Susanne Schnabel

Keyword(s):

High Resolution ◽

Sediment Deposition ◽

Aerial Images ◽

Valley Bottom ◽

Erosion And Deposition ◽

Channel Dynamics ◽

Check Dams ◽

Before And After ◽

Multi Temporal ◽

Spatio Temporal

Gullies are sources and reservoirs of sediments and perform as efficient transfers of runoff and sediments. In recent years, several techniques and technologies emerged to facilitate monitoring of gully dynamics at unprecedented spatial and temporal resolutions. Here we present a detailed study of a valley-bottom gully in a Mediterranean rangeland with a savannah-like vegetation cover that was partially restored in 2017. Restoration activities included check dams (gabion weirs and fascines) and livestock exclosure by fencing. The specific objectives of this work were: (1) to analyze the effectiveness of the restoration activities, (2) to study erosion and deposition dynamics before and after the restoration activities using high-resolution digital elevation models (DEMs), (3) to examine the role of micro-morphology on the observed topographic changes, and (4) to compare the current and recent channel dynamics with previous studies conducted in the same study area through different methods and spatio-temporal scales, quantifying medium-term changes. Topographic changes were estimated using multi-temporal, high-resolution DEMs produced using structure-from-motion (SfM) photogrammetry and aerial images acquired by a fixed-wing unmanned aerial vehicle (UAV). The performance of the restoration activities was satisfactory to control gully erosion. Check dams were effective favoring sediment deposition and reducing lateral bank erosion. Livestock exclosure promoted the stabilization of bank headcuts. The implemented restoration measures increased notably sediment deposition.

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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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