Scattering-Mechanism-Based Investigation of Optimal Combinations of Polarimetric SAR Frequency Bands for Land Cover Classification

2019 ◽  
Vol 85 (11) ◽  
pp. 799-813
Author(s):  
Zhixin Qi ◽  
Anthony Gar-On Yeh ◽  
Xia Li
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Support Vector ◽  
Frequency Variation ◽  
Polarimetric Sar ◽  
Alos Palsar ◽  
Frequency Bands ◽  
Land Cover Types ◽  
X Band ◽  
L Band

Aiming at steering the selection of optimal combinations of polarimetric SAR (PolSAR) frequency bands for different land cover classification schemes, this study investigates the land cover classification capabilities of all the possible combinations of L-band ALOS PALSAR fully PolSAR data, C-band RADARSAT-2 fully PolSAR data, and X-band TerraSAR-X HH SAR data. A method that integrates polarimetric decomposition, object-based image analysis, decision tree algorithms, and support vector machines is used for the classification. Polarimetric decomposition theorems are used to interpret the scattering mechanisms at the different frequency bands to reveal the effect mechanisms of PolSAR frequency variation on the classification capability. This study finds that (1) X-band HH SAR is not necessary for classifying the land cover types involved in this study when C- or L-band fully PolSAR are used; (2) C-band fully PolSAR alone is adequate for classifying primitive land cover types, namely, water, bare land, vegetation, and built-up areas; and (3) L-band fully PolSAR alone is adequate for distinguishing between various vegetation types, such as crops, banana trees, and forests.

Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines

2011 ◽  
Vol 49 (6) ◽  
pp. 2135-2150 ◽  
Author(s):  
Nicolas Longepe ◽  
Preesan Rakwatin ◽  
Osamu Isoguchi ◽  
Masanobu Shimada ◽  
Yumiko Uryu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Land Cover Classification ◽  
Support Vector ◽  
Alos Palsar ◽  
Vector Machines ◽  
Regional Land

scholarly journals LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE

2014 ◽  
Vol 10 (1) ◽  
Author(s):  
Katmoko Ari Sambodo ◽  
Novie Indriasari
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Feature Space ◽  
Land Cover Classification ◽  
Support Vector ◽  
Rubber Plantation ◽  
Alos Palsar ◽  
Training Samples ◽  
Separating Hyperplane

Land cover classification is  one  of  the  extensive  used  applications in  the  field  of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because  its  capability  to  penetrate  through  clouds,  haze,  and  smoke.  This  study  showed  on  an alternative  method  for  land  cover  classification  of  ALOS-PALSAR  data  using  Support  Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order  to  minimize  the  presence  of  outliers  in  the  training  samples  and  to  increase  inter-class separabilities,  prior  to  classification,  a  training  sample  selection  and  evaluation  technique  by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes  discriminated:  forest,  rubber  plantation,  mangrove  &  shrubs  with  trees,  oilpalm  &  coconut, shrubs,  cropland,  bare  soil,  settlement,  and  water.  Overall  accuracy  of  87.79%  was  obtained,  with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.

scholarly journals An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

2020 ◽  
Vol 12 (23) ◽  
pp. 3880
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Bulent Ayhan ◽  
Jiang Li ◽  
Sergio Bernabe ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Classification ◽  
Support Vector ◽  
Growth Monitoring ◽  
Land Cover Types ◽  
Imaging Detection ◽  
Better Than

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

scholarly journals Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

2014 ◽  
Vol 6 (5) ◽  
pp. 3770-3790 ◽  
Author(s):  
Wei Gao ◽  
Jian Yang ◽  
Wenting Ma
Keyword(s):  
Land Cover ◽  
Mixture Models ◽  
Land Cover Classification ◽  
Polarimetric Sar ◽  
Sar Images
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