Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuanyuan Chen ◽  
Xiufeng He ◽  
Jia Xu ◽  
Lin Guo ◽  
Yanyan Lu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Coastal Area ◽  
Optical Data ◽  
Optical Parameters ◽  
Land Classification ◽  
Polarimetric Sar ◽  
Content Type ◽  
Decision Level ◽  
Ecological Land Classification ◽  
Object Based

PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

scholarly journals A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 961 ◽  
Author(s):  
Marinalva Dias Soares ◽  
Luciano Vieira Dutra ◽  
Gilson Alexandre Ostwald Pedro da Costa ◽  
Raul Queiroz Feitosa ◽  
Rogério Galante Negri ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Data Classification ◽  
Test Site ◽  
Radar Data ◽  
Input Image ◽  
Optical Data ◽  
Land Use Classification ◽  
Object Based ◽  
Sar Imagery ◽  
Small Change

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.

The Common Denominator of Land Classification in Canada: an Ecological Approach to Mapping

1980 ◽  
Vol 56 (1) ◽  
pp. 19-20 ◽  
Author(s):  
J. S. Rowe
Keyword(s):  
Ecological Approach ◽  
Common Denominator ◽  
Land Classification ◽  
Ecological Land Classification ◽  
Common Basis ◽  
The Common

The cores and boundaries of land units are located by reference to relationships between climate, landform and biota in ecological land classification. This appeal to relationships, rather than to climate, or to geomorphology, or to soils, or to vegetation alone, provides the common basis for land classification.

Ecological land classification as a basic theme for the management of wildlands in Tennessee: A start

1996 ◽  
Vol 39 (1-3) ◽  
pp. 579-588 ◽  
Author(s):  
Glendon W. Smalley ◽  
Lorenda B. Sharber ◽  
John C. Gregory
Keyword(s):  
Land Classification ◽  
Ecological Land Classification ◽  
Basic Theme

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

2017 ◽  
Vol 38 (23) ◽  
pp. 7138-7160 ◽  
Author(s):  
Iman Khosravi ◽  
Abdolreza Safari ◽  
Saeid Homayouni ◽  
Heather McNairn
Keyword(s):  
Land Cover ◽  
Decision Tree ◽  
Land Cover Mapping ◽  
Polarimetric Sar ◽  
Sar Data

THE FOREST HABITAT TYPES IN SOUTHEASTERN MANITOBA AND THEIR APPLICATION TO FOREST MANAGEMENT

1964 ◽  
Vol 42 (10) ◽  
pp. 1417-1444 ◽  
Author(s):  
D. Mueller-Dombois
Keyword(s):  
Soil Moisture ◽  
Forest Management ◽  
Sand Dunes ◽  
Land Classification ◽  
Habitat Types ◽  
Forest Habitat ◽  
Regional Environment ◽  
Nutrient Regime ◽  
Ecological Land Classification ◽  
Silvicultural Practices

A forest ecological land classification in southeastern Manitoba resulted in the description of 14 forest habitat types, including three subtypes. These are based on silviculturally significant differences of soil moisture and nutrient regime, which are interpreted through tangible features of the three ecosystem components: vegetation, soil, and landform. The types encompass the regional environment from the driest habitats on sand dunes to the wettest in low moor bogs and from the nutritionally poorest on siliceous sandy podzols to the richest on alluvial bottomlands.The classification is to serve as a basic framework for silvicultural practices in the area. Aspects of application to current forest management are discussed.

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