Fusion of Support Vector Machines for Classification of Multisensor Data

Waske, Björn; Benediktsson, Jón Atli

doi:10.1109/tgrs.2007.898446

Cited by 349 publications

(162 citation statements)

References 34 publications

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“…This is unfortunate because such multisensor approaches can result in more reliable maps than using only one data source alone. For example, the fusion of multispectral and SAR data from an agricultural area outperforms the mono-sensoral approach in terms of the classification accuracy [60]. Overall, several studies noted higher accuracies in the differentiation of classes by the combined use of optical and SAR data in context of land use and land cover mapping [61][62][63][64], for example by minimizing spectral ambiguities and improving the characterization of phenological variability [65].…”

Section: Introductionmentioning

confidence: 99%

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

et al. 2014

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Abstract:The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, Remote Sens. 2014, 6 5280 object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors.

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Section: Introductionmentioning

confidence: 99%

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

et al. 2014

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“…The aim of SVM is to separate two classes by fitting an optimal separating hyperplane to the training data. It provides the best separation between two classes within a multidimensional feature space using only the closest training data (Vapnik, 1998;Waske and Benediktsson, 2007). This hyperplane is a decision surface and is constructed by maximizing the margin between the class boundaries.…”

Section: Support Vector Machine Classification Methodsmentioning

confidence: 99%

“…It is mostly applied to hyperspectral data however few studies are also conducted for SAR data classification (Mercier et al, 2000;Fukuda et al, 2001;Krishnapuram et al, 2003). The classification of SAR and optical datasets using SVM classifier as well as a method based on the fusion of SVMs surpassed all other parametric and nonparametric classification techniques with more than 3% accuracy improvement (Waske and Benediktsson, 2007). Many researchers have been shown that SVMs are not relatively sensitive to training sample size and it can work even with limited quantity and quality of training data (Mountrakis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Classification of various land features using RISAT-1 dual polarimetric data

Mishra

Kumar

Gupta

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH+HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23% with Kappa coefficient 0.9350.

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“…From the data availability aspect, multi-sensor/source data including optical, SAR, and GIS data have been used as inputs for classification [6][7][8][9]. To properly treat input data for classification, advanced classification methodologies such as machine learning approaches and object-based classification Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

2017

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Abstract:To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels.

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Fusion of Support Vector Machines for Classification of Multisensor Data

Cited by 349 publications

References 34 publications

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

Classification of various land features using RISAT-1 dual polarimetric data

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

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