Results 31 to 40 of about 43,365 (262)
A Hyperheuristic Approach for Unsupervised Land-Cover Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016 Unsupervised land-use/cover classification is of great interest, since it becomes even more difficult to obtain high-quality labeled data. Still considered one of the most used clustering techniques, the well-known $k$ -means plays an important role in the pattern recognition community.
João Paulo Papa +3 more
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PLoS ONE, 2012 Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed.
Dong Jiang +5 more
doaj +1 more source
A Possibility-Based Method for Urban Land Cover Classification Using Airborne Lidar Data
Remote Sensing, 2022 Airborne light detection and ranging (LiDAR) has been recognized as a reliable and accurate measurement tool in forest volume estimation, urban scene reconstruction and land cover classification, where LiDAR data provide crucial and efficient features ...
Danjing Zhao +3 more
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Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning
Remote Sensing, 2019 Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ...
Haoyu Wang +4 more
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Remote Sensing, 2021 The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM.
Yanpeng Yang +4 more
doaj +1 more source
DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management
F. Bektas Balcik, A. Karakacan Kuzucu
doaj +1 more source
Deep Learning for Land Cover Classification Using Only a Few Bands
Remote Sensing, 2020 There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and
Chiman Kwan +7 more
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Land use/land cover classification for Göktürk-2 satellite
2016 24th Signal Processing and Communication Application Conference (SIU), 2016 In this study, land-use/land-cover method for Gokturk-2 satellite was developed. Moreover, the developed method can be adapted to any satellite. The aim is to classify pixels in images captured by Gokturk-2 into four basic categories (soil, green areas, water bodies, and others), and to form a persistent mathematical model for this purpose.
Ilker Gurcan +2 more
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A Multiscale Random Forest Kernel for Land Cover Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Random forest (RF) is a popular ensemble learning method that is widely used for the analysis of remote sensing images. RF also has connections with the kernel-based method. Its tree-based structure can generate an RF kernel (RFK) that provides an alternative to common kernels such as radial basis function (RBF) in kernel-based methods such as support ...
Azar Zafari +2 more
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Land Cover Image Classification
CoRRLand Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis.
Antonio Rangel +3 more
openaire +2 more sources