scholarly journals Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

Information ◽  
2017 ◽  
Vol 8 (4) ◽  
pp. 147 ◽  
Author(s):  
André Mora ◽  
Tiago Santos ◽  
Szymon Łukasik ◽  
João Silva ◽  
António Falcão ◽  
...  
Keyword(s):  
Land Cover ◽  
Comparative Study ◽  
Computational Intelligence ◽  
Land Cover Classification ◽  
Multispectral Data

scholarly journals Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽  
2018 ◽  
Vol 2 (20) ◽  
pp. 1280 ◽  
Author(s):  
Laura Fragoso-Campón ◽  
Elia Quirós ◽  
Julián Mora ◽  
José Antonio Gutiérrez ◽  
Pablo Durán-Barroso
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Accuracy Enhancement ◽  
Multispectral Data ◽  
Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

scholarly journals FEASIBILITY OF MULTISPECTRAL AIRBORNE LASER SCANNING FOR LAND COVER CLASSIFICATION, ROAD MAPPING AND MAP UPDATING

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 119-122 ◽  
Author(s):  
L. Matikainen ◽  
K. Karila ◽  
J. Hyyppä ◽  
E. Puttonen ◽  
P. Litkey ◽  
...  
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Land Cover Classification ◽  
Aerial Images ◽  
Multispectral Data ◽  
Airborne Laser ◽  
First Results ◽  
Road Mapping

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015–2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

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