Improved Nonlinear Manifold Learning for Land Cover Classification via Intelligent Landmark Selection

2006 ◽  
Author(s):  
Y. Chen ◽  
M. Crawford ◽  
J. Ghosh
Keyword(s):  
Land Cover ◽  
Manifold Learning ◽  
Land Cover Classification ◽  
Landmark Selection

A manifold learning approach to urban land cover classification with optical and radar data

2018 ◽  
Vol 172 ◽  
pp. 11-24 ◽  
Author(s):  
Hongsheng Zhang ◽  
Jiang Li ◽  
Ting Wang ◽  
Hui Lin ◽  
Zezhong Zheng ◽  
...  
Keyword(s):  
Land Cover ◽  
Manifold Learning ◽  
Land Cover Classification ◽  
Radar Data ◽  
Urban Land ◽  
Learning Approach ◽  
Urban Land Cover

scholarly journals A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion

2019 ◽  
Vol 11 (6) ◽  
pp. 681 ◽  
Author(s):  
Jingliang Hu ◽  
Danfeng Hong ◽  
Yuanyuan Wang ◽  
Xiao Zhu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Manifold Learning ◽  
Land Cover Classification ◽  
Data Availability ◽  
Data Sources ◽  
Land Use Land Cover ◽  
Data Alignment ◽  
Comparative Review ◽  
Manifold Alignment

In remote sensing, hyperspectral and polarimetric synthetic aperture radar (PolSAR) images are the two most versatile data sources for a wide range of applications such as land use land cover classification. However, the fusion of these two data sources receive less attention than many other, because of their scarce data availability, and relatively challenging fusion task caused by their distinct imaging geometries. Among the existing fusion methods, including manifold learning-based, kernel-based, ensemble-based, and matrix factorization, manifold learning is one of most celebrated techniques for the fusion of heterogeneous data. Therefore, this paper aims to promote the research in hyperspectral and PolSAR data fusion, by providing a comprehensive comparison between existing manifold learning-based fusion algorithms. We conducted experiments on 16 state-of-the-art manifold learning algorithms that embrace two important research questions in manifold learning-based fusion of hyperspectral and PolSAR data: (1) in which domain should the data be aligned—the data domain or the manifold domain; and (2) how to make use of existing labeled data when formulating a graph to represent a manifold—supervised, semi-supervised, or unsupervised. The performance of the algorithms were evaluated via multiple accuracy metrics of land use land cover classification over two data sets. Results show that the algorithms based on manifold alignment generally outperform those based on data alignment (data concatenation). Semi-supervised manifold alignment fusion algorithms performs the best among all. Experiments using multiple classifiers show that they outperform the benchmark data alignment-based algorithms by ca. 3% in terms of the overall classification accuracy.

Land cover classification for the monitoring of winter grain crops within the Kyiv oblast and Mykolaiv oblast (Ukraine) with the use of MODIS data

2009 ◽  
Vol 15 (5) ◽  
pp. 16-23
Author(s):  
O.I. Sakhatsky ◽  
◽  
G.M. Zholobak ◽  
A.A. Makarova ◽  
O.A. Apostolov ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Grain Crops ◽  
Modis Data

Data post processing

2018 ◽  
Author(s):  
Serge A. Wich ◽  
Lian Pin Koh
Keyword(s):  
Land Cover ◽  
Structure From Motion ◽  
Land Cover Classification ◽  
Post Processing ◽  
Surface Models

This chapter discusses how data that have been collected with drones can be used to derive orthomosaics and digital surface models through structure-from-motion software and how these can be processed further for land-cover classification or into vegetation metrics. Some examples of the various programs are provided as well. The chapter ends with a discussion on the approaches that have been used to automate counts of animals in drone images.

Sign in / Sign up

Export Citation Format

Share Document