scholarly journals Remote Sensing of the Coastline Variation of the Guangdong–Hongkong–Macao Greater Bay Area in the Past Four Decades

2021 ◽  
Vol 9 (12) ◽  
pp. 1318
Author(s):  
Ruirui Hu ◽  
Lijun Yao ◽  
Jing Yu ◽  
Pimao Chen ◽  
Dongliang Wang
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Dynamic Monitoring ◽  
Visual Interpretation ◽  
The Past ◽  
Bay Area ◽  
Landsat Satellite ◽  
Almost All

In this study, a combination of example-based feature extraction and visual interpretation was applied to analyze the coastline variations in the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) from the past four decades based on the Landsat satellite remote sensing image data from 1987–2018, using ENVI and ArcGIS software. The results showed that the total length of the coastline of the GHMGBA increased in the past four decades, rising from 1291 km in 1987 to 1411 km in 2018. Among these, artificial coastline increased by 450 km, while the other coastline types decreased. The type of coastline that decreased the most was bedrock coastline, by a total of 172 km. The silty coastline disappeared, and almost all of it was converted to artificial coastline. Variations in the coastline of the GHMGBA were mainly connected to human activities and showed an overall trend of advancing towards the ocean. Dynamic monitoring of coastline variations can provide a reference for the protection of natural resources, sustainable marine development and rational planning of the coastal zone.

Monitoring Desertification Process in Songnen Sandy Land during the past 10 Years

2012 ◽  
Vol 518-523 ◽  
pp. 4740-4744
Author(s):  
Wei Yang ◽  
Shu Wen Zhang
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Image Data ◽  
Remote Sensing Image ◽  
Sandy Land ◽  
The Past ◽  
Desertification Process ◽  
Result Show

In this paper, we used MODIS remote sensing image data of Songnen Sandy Land in July 2000 and 2010, extracted the value of MSAVI and vegetation cover index. Based on their values, degrees of desertification were classified including: un-desertification, micro-desertification, mild desertification, moderate desertification and severe desertification. The result show that the area of the desertification decreased in the past 10 years. The desertification is under a decreasing trend.

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Visual Categorization ◽  
Class Differences ◽  
Fine Grained ◽  
Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

scholarly journals A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 731-736
Author(s):  
Y. Xu ◽  
X. Hu ◽  
Y. Wei ◽  
Y. Yang ◽  
D. Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Heterogeneity ◽  
Satellite Remote Sensing ◽  
Sampling Method ◽  
National Level ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Large Scope

<p><strong>Abstract.</strong> The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.</p>

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