Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion

Waske, Björn; Linden, Sebastian van der

doi:10.1109/tgrs.2008.916089

Cited by 185 publications

(88 citation statements)

References 43 publications

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“…RF performs efficiently with large data sets, is robust to outliers and overfitting, and its parameter selection is user-friendly [77]. The RF has demonstrated excellent performance in terms of classifying diverse remote sensing data sets [44,56,[78][79][80] and especially joint optical and radar data sets [63,81].…”

Section: Random Forest Classifiermentioning

confidence: 99%

“…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]. However, none of these studies have assessed how the combined use of optical and SAR data can advance the mapping of management intensity of cropland.…”

Section: Introductionmentioning

confidence: 99%

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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: Random Forest Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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“…The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48,68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

“…N) are support vectors, a i and b are specific parameters of the hyperplane, and K (X, X i ) is the kernel function used to construct machines with different types of decision surfaces in the feature space. The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48,68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.…”

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confidence: 99%

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

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Abstract:Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

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“…Data fusion can be performed at three different levels: pixel, feature, and decision. With the characteristics of high openness and fault-tolerance, the decision-level approaches have the potential to improve classification accuracy [19][20][21][22]. The popular decision-level approaches include the Bayesian methods [23], Dempster-Shafer (D-S) evidence theory [24], and fuzzy-logic [25].…”

Section: Introductionmentioning

confidence: 99%

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Xie

Zhang

et al. 2015

Remote Sensing

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Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P-and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer's accuracy of 96% and user's accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring.

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Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion

Cited by 185 publications

References 43 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

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

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