scholarly journals PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain

2017 ◽  
Vol 9 (7) ◽  
pp. 660 ◽  
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Rotation Domain

scholarly journals POLSAR LAND COVER CLASSIFICATION BASED ON HIDDEN POLARIMETRIC FEATURES IN ROTATION DOMAIN AND SVM CLASSIFIER

2017 ◽  
Vol IV-2/W4 ◽  
pp. 485-492
Author(s):  
C.-S. Tao ◽  
S.-W. Chen ◽  
Y.-Z. Li ◽  
S.-P. Xiao
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Classification Scheme ◽  
Land Cover Classification ◽  
Line Of Sight ◽  
Support Vector ◽  
Svm Classifier ◽  
Multi Temporal ◽  
Information Exploration ◽  
Rotation Domain

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as <i>H&amp;thinsp;/&amp;thinsp;Ani&amp;thinsp;/&amp;thinsp;<span style="text-decoration: overline">α</span>&amp;thinsp;/&amp;thinsp;Span</i> are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets’ scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed classification scheme is 94.91&amp;thinsp;%, while that with the conventional classification scheme is 93.70&amp;thinsp;%. Moreover, for multi-temporal UAVSAR data, the averaged overall classification accuracy with the proposed classification scheme is up to 97.08&amp;thinsp;%, which is much higher than the 87.79&amp;thinsp;% from the conventional classification scheme. Furthermore, for multitemporal PolSAR data, the proposed classification scheme can achieve better robustness. The comparison studies also clearly demonstrate that mining and utilization of hidden polarimetric features and information in the rotation domain can gain the added benefits for PolSAR land cover classification and provide a new vision for PolSAR image interpretation and application.

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.

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

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