Comparison of Coastline Extraction Methods and Block Classification Method to Extract Coastlines based on Remote Sensing

2021 ◽  
Author(s):  
Zongmei Li ◽  
Hongmei Chen ◽  
Qin Nie
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Extraction Methods ◽  
Classification Method ◽  
Segmentation Method ◽  
Visual Interpretation ◽  
Threshold Segmentation ◽  
Detection Algorithms ◽  
Block Classification ◽  
Interpretation Method

Abstract Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

scholarly journals Comparison of remote sensing extraction methods for glacier firn line-considering Urumqi Glacier No.1 as the experimental area

2020 ◽  
Vol 218 ◽  
pp. 04024
Author(s):  
Yanjun Zhao ◽  
Jun Zhao ◽  
Xiaoying Yue ◽  
Yanqiang Wang
Keyword(s):  
Remote Sensing ◽  
Mass Balance ◽  
Extraction Methods ◽  
High Accuracy ◽  
Single Band ◽  
Visual Interpretation ◽  
Negative Mass ◽  
Inversion Methods ◽  
Experimental Area ◽  
Normalized Difference Snow Index

In mid-latitude glaciers, the altitude of the snowline at the end of the ablating season can be used to indicate the equilibrium line, which can be used as an approximation for it. In this paper, Urumqi Glacier No.1 was selected as the experimental area while Landsat TM/ETM+/OLI images were used to analyze and compare the accuracy as well as applicability of the visual interpretation, Normalized Difference Snow Index, single-band threshold and albedo remote sensing inversion methods for the extraction of the firn lines. The results show that the visual interpretation and the albedo remote sensing inversion methods have strong adaptability, alonger with the high accuracy of the extracted firn line while it is followed by the Normalized Difference Snow Index and the single-band threshold methods. In the year with extremely negative mass balance, the altitude deviation of the firn line extracted by different methods is increased. Except for the years with extremely negative mass balance, the altitude of the firn line at the end of the ablating season has a good indication for the altitude of the balance line.

Application of Remote Sensing Technology in Tianjin Binhai New Area Coastal Zone Monitoring

2010 ◽  
Author(s):  
Juan Wang ◽  
Zhiguo Bu ◽  
Zhongqiang Li
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Large Scale ◽  
Influential Factor ◽  
Visual Interpretation ◽  
Economic Activities ◽  
The Past ◽  
Sensing Technology ◽  
Temporal Aspects ◽  
Tianjin City

The coastal zone is the belt influenced by land and ocean interactions, as well as human factors. So its evolution depends not only on natural factors but also on human socio-economic activities. It has very good instructive meaning to provide timely accurate coastal zone changeing information for exploiting and protecting the coast. Using 5 periods’ remote sensing images covering 20 years from 1987 to 2008 of Tianjin city, this paper extracted the coastline and the wetlands from different years utilizing different methods and techniques of data image processing and visual interpretation based on the characteristic of each RS image. The paper analyzed the law of the coastline and the wetlands changes in both spatial and temporal aspects, and then discussed the major influential factor to the changes by analyzing natural and artificial factors. The results indicated that the total coastline and the natural coastline increased, while the artificial seashore and wetlands decreased in large scale in the 20 years, due to the development of the coastal industry. Thanks to the protection and reinstatement for wetlands, the area of wetlands increased in the past two years.

scholarly journals Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

2020 ◽  
Vol 12 (19) ◽  
pp. 3159
Author(s):  
Angel Fernandez-Carrillo ◽  
Antonio Franco-Nieto ◽  
Erika Pinto-Bañuls ◽  
Miguel Basarte-Mena ◽  
Beatriz Revilla-Romero
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Cover ◽  
Performance Metrics ◽  
Temporal Dynamics ◽  
Forest Products ◽  
Future Research ◽  
Forest Types ◽  
Visual Interpretation ◽  
Sampling System

The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OAMSF = 96.3%; DCMSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OEMSF = 2.4%; CEMSF = 4.5%; relBMSF = 2%), while the other products showed on average lower accuracies (OAHRL = 89.2%; OAFNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OEMSF = 9.5%; OEHRL = 59.5%; OEFNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets.

The Comparative Study on the Automatic Extraction Methods of Artificial Channel Based on ETM Image

2012 ◽  
Vol 500 ◽  
pp. 506-510
Author(s):  
Cheng Lu ◽  
Qiang Li ◽  
Li Jun Yu
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Large Scale ◽  
Water Shortage ◽  
Extraction Methods ◽  
Scientific Basis ◽  
Wetland Protection ◽  
Gis Technology ◽  
Band Method ◽  
Spatio Temporal

It’s urgent for China to solve the water shortage. Quickly and accurately extracting water resources from satellite remote sensing has become an important means of the investigation and monitoring of water resources and wetland protection. The fact that the spatio-temporal span of channel is large made the investigation difficult especially by the conventional way. Remote Sensing plays an increasing important role in the water resources protection with advantages of large scale, integration, dynamics and fastness. The RS images recorded the truth of the surface landscape in history and can reflect the distributing and the status quo of the channel in different courses of history. The article analyses the spectral and spatial feature of channel in ETM images in order to extraction the channel automatically with the different RS methods combined with GIS technology. A comparison among these methods is made. In addition, the article assesses the results of single-band method and multi-band method qualitatively and quantitatively. This study provide a scientific basis for the protection of water resource.

scholarly journals Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree

2020 ◽  
Vol 12 (5) ◽  
pp. 783 ◽  
Author(s):  
Wenjie Lin ◽  
Yu Li
Keyword(s):  
Remote Sensing ◽  
Parallel Processing ◽  
High Resolution ◽  
Spanning Tree ◽  
Large Scale ◽  
Minimum Spanning Tree ◽  
Process Efficiency ◽  
Qualitative Assessment ◽  
Segmentation Method ◽  
High Resolution Images

With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method.

scholarly journals Multi-Modality and Multi-Scale Attention Fusion Network for Land Cover Classification from VHR Remote Sensing Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3771
Author(s):  
Tao Lei ◽  
Linze Li ◽  
Zhiyong Lv ◽  
Mingzhe Zhu ◽  
Xiaogang Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Large Scale ◽  
Feature Fusion ◽  
Network Models ◽  
Land Cover Classification ◽  
Extraction Methods ◽  
Feature Representation ◽  
Remote Sensing Images ◽  
Multi Scale

Land cover classification from very high-resolution (VHR) remote sensing images is a challenging task due to the complexity of geography scenes and the varying shape and size of ground targets. It is difficult to utilize the spectral data directly, or to use traditional multi-scale feature extraction methods, to improve VHR remote sensing image classification results. To address the problem, we proposed a multi-modality and multi-scale attention fusion network for land cover classification from VHR remote sensing images. First, based on the encoding-decoding network, we designed a multi-modality fusion module that can simultaneously fuse more useful features and avoid redundant features. This addresses the problem of low classification accuracy for some objects caused by the weak ability of feature representation from single modality data. Second, a novel multi-scale spatial context enhancement module was introduced to improve feature fusion, which solves the problem of a large-scale variation of objects in remote sensing images, and captures long-range spatial relationships between objects. The proposed network and comparative networks were evaluated on two public datasets—the Vaihingen and the Potsdam datasets. It was observed that the proposed network achieves better classification results, with a mean F1-score of 88.6% for the Vaihingen dataset and 92.3% for the Potsdam dataset. Experimental results show that our model is superior to the state-of-the-art network models.

scholarly journals Remote Sensing and Gis Based Land Use and Land Cover Information of Medchal Mandalof Medchal District

2019 ◽  
Vol 9 (2) ◽  
pp. 4682-4685
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Town Planning ◽  
Environmental Study ◽  
Land Resources ◽  
Visual Interpretation ◽  
Gis And Remote Sensing ◽  
Digital Format

It is exceptionally significant to use GIS and remote sensing application for proficient need in daily life. Upcoming and contemporary technologies like data processing, earth observation geodata processing and investigation are necessary for the researcher for the development of the society on a large scale. Remote sensing information data both in digital format and image format is utilized for retrieving the information about land resources by using (DIP) digital Interpretation Techniques and (VIP) Visual interpretation techniques Techniques. The foremost objective of the given study area is to Setup land use and land cover information system to evaluate land resources by by means of GIS Remote Sensing at Arc GIS10.2.1 platform of MedchalMandal. GIS and Remote Sensing information is the ultimate solution for the coverage of large area. Different types of layers are created from Remote Sensing images data and ArcGIS 10.2.1 Software. In the present study analysis is carried out by primary information which was generated from remote sensing data. GIS is Decision support system which helps planners and Decision makers to take correct decision for sustainable development, it also helps developers, engineers in environmental study, town planning and resource management.

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