Coastline Detection from Remote Sensing Image Based on K-Mean Cluster and Distance Transform Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1567-1571 ◽  
Author(s):  
Ying Liu
Keyword(s):  
Remote Sensing ◽  
Cluster Algorithm ◽  
Remote Sensing Image ◽  
Distance Transform ◽  
Land Area ◽  
Remote Sensing Technique ◽  
Novel Approach ◽  
Sea Area

Using remote sensing technique to determine coastline's position has been received vital attention. This paper presents a novel approach for detecting coastline of remote sensing image based on K-Means cluster and Distance Transform algorithm. K-Means cluster algorithm divides the image into two regions-water and land area. Then to extract the sea area by distance transfoming. Finally, the coastline will be detected by edge traking. Results showed that the method proposed in this paper have good performance in accuracy and completeness.

scholarly journals Near-infrared remote sensing of Los Angeles trace gas distributions from a mountaintop site

2014 ◽  
Vol 7 (3) ◽  
pp. 713-729 ◽  
Author(s):  
D. Fu ◽  
T. J. Pongetti ◽  
J.-F. L. Blavier ◽  
T. J. Crawford ◽  
K. S. Manatt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Los Angeles ◽  
Greenhouse Gases ◽  
Near Infrared ◽  
Global Climate ◽  
Ghg Emissions ◽  
Anthropogenic Source ◽  
Los Angeles Basin ◽  
Remote Sensing Technique ◽  
Novel Approach

Abstract. The Los Angeles basin is a significant anthropogenic source of major greenhouse gases (CO2 and CH4) and the pollutant CO, contributing significantly to regional and global climate change. We present a novel approach for monitoring the spatial and temporal distributions of greenhouse gases in the Los Angeles basin using a high-resolution spectroscopic remote sensing technique. A new Fourier transform spectrometer called CLARS-FTS has been deployed since May, 2010, at Jet Propulsion Laboratory (JPL)'s California Laboratory for Atmospheric Remote Sensing (CLARS) on Mt. Wilson, California, for automated long-term measurements of greenhouse gases. The instrument design and performance of CLARS-FTS are presented. From its mountaintop location at an altitude of 1673 m, the instrument points at a programmed sequence of ground target locations in the Los Angeles basin, recording spectra of reflected near-IR solar radiation. Column-averaged dry-air mole fractions of greenhouse gases (XGHG) including XCO2, XCH4, and XCO are retrieved several times per day for each target. Spectra from a local Spectralon® scattering plate are also recorded to determine background (free tropospheric) column abundances above the site. Comparisons between measurements from LA basin targets and the Spectralon® plate provide estimates of the boundary layer partial column abundances of the measured species. Algorithms are described for transforming the measured interferograms into spectra, and for deriving column abundances from the spectra along with estimates of the measurement precision and accuracy. The CLARS GHG measurements provide a means to infer relative, and possibly absolute, GHG emissions.

scholarly journals IMPROVING GLOBALlAND30 ARTIFICIAL TYPE EXTRACTION ACCURACY IN LOW-DENSITY RESIDENTS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1305-1308
Author(s):  
Lili Hou ◽  
Ling Zhu ◽  
Shu Peng ◽  
Zhenlei Xie ◽  
Xu Chen
Keyword(s):  
Remote Sensing ◽  
Thermal Radiation ◽  
Remote Sensing Image ◽  
Low Density ◽  
Cultivated Land ◽  
Bare Land ◽  
Artificial Surface ◽  
Nighttime Light ◽  
Data Source ◽  
Sea Area

GlobalLand 30 is the first 30m resolution land cover product in the world. It covers the area within 80°N and 80°S. There are ten classes including artificial cover, water bodies, woodland, lawn, bare land, cultivated land, wetland, sea area, shrub and snow,. The TM imagery from Landsat is the main data source of GlobalLand 30. In the artificial surface type, one of the omission error happened on low-density residents’ part. In TM images, hash distribution is one of the typical characteristics of the low-density residents, and another one is there are a lot of cultivated lands surrounded the low-density residents. Thus made the low-density residents part being blurred with cultivated land. In order to solve this problem, nighttime light remote sensing image is used as a referenced data, and on the basis of NDBI, we add TM6 to calculate the amount of surface thermal radiation index TR-NDBI (Thermal Radiation Normalized Difference Building Index) to achieve the purpose of extracting low-density residents. The result shows that using TR-NDBI and the nighttime light remote sensing image are a feasible and effective method for extracting low-density residents’ areas.

A User Preferences Modeling for Intellegent Dissemination of Remote Sensing Image

2014 ◽  
Vol 519-520 ◽  
pp. 548-552
Author(s):  
Chun Hui Zhou ◽  
Gou Jun Luo ◽  
Di Chen ◽  
Yu Xia ◽  
Li Wen Huang
Keyword(s):  
Remote Sensing ◽  
Time Factors ◽  
User Profile ◽  
Remote Sensing Image ◽  
Computing Methods ◽  
User Preferences ◽  
Time Space ◽  
Novel Approach ◽  
User Demand ◽  
Demand Preference

In order to achieve the intelligent dissemination of remote sensing image, the primary task is to establish a suitable user profile. In this paper, we proposed a novel approach of modeling user demand preferences, and took into account the multiple interests and the time factors. And we presented some computing methods of feature preference including time, space, image parameters, etc. At last, the simulation example shows the feasibility and effectiveness of the designed user demand preference model.

scholarly journals Land Classification for Optimal Use of Land by Remote Sensing and GIS Technology

2012 ◽  
Vol 5 (1) ◽  
pp. 229-237
Author(s):  
MF Haque ◽  
MS Ali ◽  
MA Haq ◽  
MMR Akhand
Keyword(s):  
Remote Sensing ◽  
Aerial Photographs ◽  
Land Area ◽  
Gis Technology ◽  
Land Type ◽  
Remote Sensing Technique ◽  
Growing Seasons ◽  
Multi Temporal ◽  
Crop Types ◽  
Rabi Crops

In this paper, attempt has been made to prepare landuse map for the district of Brahmanbaria, situated in the east-central part of        Bangladesh using remote sensing technique. The multi-spectral Landsat TM data for 3 November 2002, 15 December 2004, 01 February 2002, 14 March 2003 and some aerial photographs of December 2000 have been used for land-use mapping for major three crops namely aman rice (late July - early November), winter (rabi) crops and winter (boro) rice. The imagery covers the growing seasons of the above crops, where multi-spectral and multi-temporal signatures for the green vegetations have been shown in spatial domain. The interpretation of the aerial photographs have also been performed and prepared GIS layers containing the water bodies and settlements. All the signature files including the interpretations of aerial photographs have been combined to produce a composite file in GIS layers. These layers were then combined to prepare the landuse maps including the three major crops cultivated round the year. Moreover, the landuse map of Akhaura upazila was compared with the land-type map and a relation of the landuse with the land-type has also been derived. The extracted feature files corresponding to spectral signatures have been overlaid to estimate the distribution of three major crop types in the study area. The result implies that all the three major crops like, aman, rabi and boro were cultivated in the same land which was 9.2% of total land area. Similarly, areas under double and single crops were also estimated and the result revealed that all the three crop types cover 68.3% of the total land area of Brahmanbaria district. DOI: http://dx.doi.org/10.3329/jesnr.v5i1.11587 J. Environ. Sci. & Natural Resources, 5(1): 229-237, 2012  

scholarly journals Application of UAV-Low Altitude Remote Sensing System in Sea Area Supervision

2021 ◽  
Vol 25 (1) ◽  
pp. 65-68
Author(s):  
Rong Chen
Keyword(s):  
Remote Sensing ◽  
National Security ◽  
Unmanned Aerial Vehicles ◽  
Remote Sensing Image ◽  
Aerial Images ◽  
Marine Resources ◽  
Sensing System ◽  
Low Altitude ◽  
Remote Sensing Image Processing ◽  
Sea Area

The sea area supervision is the premise and guarantee of safeguarding national security, protecting national sovereignty, and realizing the development of marine resources, and its importance is self-evident. To carry out the national sea area work more efficiently, this study designed low altitude-Unmanned Aerial Vehicles (UAV) remote sensing system applied to the sea area supervision and analyzed the remote sensing photography technology and remote sensing image processing technology. Experiments verified the effectiveness of the system. The research results show that the UAV-based low altitude remote sensing system can extract high-precision sea area information through aerial images’ interpretation. It is hoped that this study can provide some reference for improving the efficiency of current sea area supervision.

scholarly journals A Novel Approach to Developing a Supervised Spatial Decision Support System for Image Classification: A Study of Paddy Rice Investigation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shih-Hsun Chang
Keyword(s):  
Remote Sensing ◽  
Pso Algorithm ◽  
Classification Performance ◽  
Paddy Rice ◽  
Spatial Decision Support System ◽  
Linear Discriminant ◽  
Remote Sensing Technique ◽  
Texture Information ◽  
Novel Approach ◽  
Ancillary Information

Paddy rice area estimation via remote sensing techniques has been well established in recent years. Texture information and vegetation indicators are widely used to improve the classification accuracy of satellite images. Accordingly, this study employs texture information and vegetation indicators as ancillary information for classifying paddy rice through remote sensing images. In the first stage, the images are attained using a remote sensing technique and ancillary information is employed to increase the accuracy of classification. In the second stage, we decide to construct an efficient supervised classifier, which is used to evaluate the ancillary information. In the third stage, linear discriminant analysis (LDA) is introduced. LDA is a well-known method for classifying images to various categories. Also, the particle swarm optimization (PSO) algorithm is employed to optimize the LDA classification outcomes and increase classification performance. In the fourth stage, we discuss the strategy of selecting different window sizes and analyze particle numbers and iteration numbers with corresponding accuracy. Accordingly, a rational strategy for the combination of ancillary information is introduced. Afterwards, the PSO algorithm improves the accuracy rate from 82.26% to 89.31%. The improved accuracy results in a much lower salt-and-pepper effect in the thematic map.

scholarly journals The Spatial-Temporal Characteristics of Urban Construction Land Expansion in Wuhan, Central China

2018 ◽  
Vol 53 ◽  
pp. 03071
Author(s):  
Qianwen Han ◽  
Yan Yu ◽  
Shanshan Hu ◽  
Yue Chen ◽  
Yuanyuan Ke ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Central China ◽  
Contribution Rate ◽  
Land Area ◽  
Wuhan City ◽  
Construction Land ◽  
Urban Construction ◽  
Temporal Features ◽  
Expansion Speed

Data of construction land in Wuhan city were obtained from remote sensing image in different periods. Based on the spatial analysis function of GIS, the characteristics of construction land expansion were identified by several methods to analyse the spatial-temporal features of Wuhan city area from 1995 to 2015, which included expansion speed, expansion elasticity, contribution rate of expansion, centre-ofgravity shift, and quadrant orientation. The results showed that the construction land area increased gradually from 1995 to 2015 in Wuhan city, and the expansion speed first increased and then decreased. The direction of construction land expansion in Wuhan city was expanded to the southwest significantly. The construction land expansion can’t meet the needs of population growth. Jiangxia district contributed most to the expansion of Wuhan city.

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