Intelligent Land Cover Detection in Multi-sensor Satellite Images

2018 ◽  
pp. 118-128
Author(s):  
R. Jenice Aroma ◽  
Kumudha Raimond
Keyword(s):  
Land Cover ◽  
Satellite Images

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Yuxiang Lin ◽  
Wei Dong ◽  
Yi Gao ◽  
Tao Gu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Low Cost ◽  
Tracking Error ◽  
Location Based Services ◽  
Distance Constraint ◽  
Combination Strategy ◽  
Land Cover Types ◽  
High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

How up-scaling of remote-sensing images affects land-cover classification by comparison with multiscale satellite images

2018 ◽  
Vol 40 (7) ◽  
pp. 2784-2810 ◽  
Author(s):  
Kaijian Xu ◽  
Qingjiu Tian ◽  
Yanjun Yang ◽  
Jibo Yue ◽  
Shaofei Tang
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Images ◽  
Land Cover Classification ◽  
Remote Sensing Images ◽  
Up Scaling

scholarly journals Land area increase in Ukrainian part of the Danube delta

2021 ◽  
Author(s):  
V. M. Starodubtsev ◽  
◽  
M. M. Ladyka ◽  
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Extreme Event ◽  
Relative Equilibrium ◽  
Danube River ◽  
Danube Delta ◽  
Land Area ◽  
Area Increase ◽  
Landsat Satellite ◽  
Quantitative Indicators

The quantitative indicators of land growth in the Ukrainian part of the Danube delta are considered. Comparison of Landsat satellite images in three key areas of the delta showed that for the period 1975-2020 the area of wetlands at the mouth of the Сhilia channel increased by 1448 hectares due to the accumulation of sediments between the Starostambul and Limba branches and their overgrowth with vegetation. In the area of the Bystroe channel, the area of new lands increased by 1037 hectares due to the artificial deepening of this channel for the Ukrainian ships passage into the Danube River and the deposition of sediments along the coast. A slightly smaller increase in land cover (797 ha) was found in the northern part of the coast of the Ukrainian part of the delta, where saline and carbonate soils are formed. In the future, active land growth is expected in the Musura bay between the mouths of the Starostambul and Sulina branches, ie at the contact of Ukraine and Romania. Some changes in these parameters are expected after a powerful flood in 2021, which will become known after the establishment of a relative equilibrium between the processes of accumulation and erosion after this extreme event.

scholarly journals Experiment of Multispectral Images using Spectral Angle Mapper Algorithm for Land Cover Classification

2019 ◽  
Vol 8 (6S4) ◽  
pp. 96-99
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Multispectral Images ◽  
Rapid Urbanization ◽  
Spectral Angle Mapper ◽  
Time Period ◽  
Steep Decline ◽  
Spectral Angle

Urbanization plays a key role in the health of the water bodies in any region. In a rapidly growing country like India, especially Bangalore district, rapid urbanization has seen a steep decline in the number of water bodies the region is famous for. In this paper, Land Use and Land Cover change is analysed for the remotely sensed images of Bangalore District using Spectral Angle Mapper Algorithm. Data for the purpose of analysis was obtained from BHUVAN (NRSC, ISRO). The study area is Bangalore District and data was collected from the time period 2008-2016. The major classes used in the classification are Land(Built-up), water bodies (Lakes), Vegetation (Gardens), Soil (Barren and fertile). The satellite images and the accompanying classification algorithms indicate that the percentage of water bodies have drastically shrunk (from 2.9% in 2008to1.8% in 2016) in the area of study. The results of this study can be used by the civic authorities to implement decisions to conserve the water bodies in the area.

scholarly journals GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES

2011 ◽  
Vol 21 (1) ◽  
pp. 19 ◽  
Author(s):  
Catherine Mering ◽  
Franck Chopin
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Arid Regions ◽  
Satellite Images ◽  
Distribution Analysis ◽  
Granulometric Analysis ◽  
High Resolution Satellite Images ◽  
Thematic Mapping ◽  
Urban Settlements

A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.

Sign in / Sign up

Export Citation Format

Share Document