Classifying Multifrequency Fully Polarimetric Imagery With Multiple Sources of Statistical Evidence and Contextual Information

Frery, Alejandro C.; Correia, Antonio Henrique; Freitas, Corina da Costa

doi:10.1109/tgrs.2007.903828

Cited by 113 publications

(91 citation statements)

References 41 publications

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“…The spatial resolution and the number of looks can affect the distinction of objects in the image, the quantity of noise and the statistics, which influences the distinction of targets when statistical parameters are involved in the analysis (Frery et al, 2007). Moreover, the variation of the incidence angle affects the capacity of penetration of the microwave radiation into the canopy, being one of the parameters of the sensor that most influences backscattering and the appearance of features included in the image (Lewis and Henderson, 1998).…”

Section: Discussionmentioning

confidence: 99%

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Silva

Rudorff

Formaggio

et al. 2012

Sci. agric. (Piracicaba, Braz.)

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Section: Discussionmentioning

confidence: 99%

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Silva

Rudorff

Formaggio

et al. 2012

Sci. agric. (Piracicaba, Braz.)

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“…For PolSAR data, the ENL and shape parameter have been traditionally estimated separately for each polarimetric channel and then averaged, as in [38]. However, this method ignores the polarimetric information and is unreliable, whereas the direct use of the coherency matrix or covariance matrix obtains good results.…”

Section: Polsar K Distributionmentioning

confidence: 99%

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Chen

Yang

et al. 2016

Remote Sensing

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Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.

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“…However, these classifiers are pixel-based and, therefore, due to speckle noise, which is inherent to radar data, the results obtained from these type of classifiers are usually very noisy. Contextual classifiers, as the Iterative Conditional Modes (ICM) [2], incorporate context.…”

Section: Introductionmentioning

confidence: 99%

Land cover discrimination at Brazilian Amazon using region based classifier and stochastic distance

Silva

Pereira

Sant’Anna

et al. 2011

2011 IEEE International Geoscience and Remote Sensing Symposium

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Given the different nature of optical and radar data, it is reasonable the idea that each type of data can contribute in complementary ways for different applications. This paper aims at analyzing the potential joint usage of optical and Synthetic Aperture Radar (SAR) data for land use and land cover classification in a region located in the Brazilian Amazon. To achieve this objective, we evaluated regionbased classifications using separated and fused optical and SAR data. Data were images from the Landsat 5/TM sensor and amplitude multipolarized images from the ALOS/PALSAR sensor. The images were classified using a region-based classifier based on the Bhattacharyya distance between Gaussian distributions. The TM data alone is better for classify land cover classes with occurrence of trees or shrubs, while SAR data contribute to improve the classification results in low vegetated areas.

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Classifying Multifrequency Fully Polarimetric Imagery With Multiple Sources of Statistical Evidence and Contextual Information

Cited by 113 publications

References 41 publications

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Land cover discrimination at Brazilian Amazon using region based classifier and stochastic distance

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