Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Soh, Leen Kiat; Tsatsoulis, Costas

doi:10.1109/36.752194

Cited by 1,039 publications

(509 citation statements)

References 58 publications

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“…The results of this procedure depend on several factors such as the size of the sliding window, the co-occurrence distance, and the quantization levels (Shokr, 1991;Soh and Tsatsoulis, 1999;Clausi, 2002). In order to test the effects of these parameters on the classification accuracy, texture features were calculated for the window sizes 16, 32, 64, and 128 pixels using different co-occurrence distances and varying the number of quantized gray levels ( Table 1).…”

Section: Calculation Of Texture Featuresmentioning

confidence: 99%

“…In particular, discrimination between calm open water and smooth first-year ice, as well as between windy open water and young ice with frost flowers or multiyear ice, can be problematic. Including additional image characteristics like image texture, tone, and spatial structures can improve the classification results significantly (Shokr, 1991;Soh and N. Zakhvatkina et al: Operational algorithm for ice-water classification Tsatsoulis, 1999;Clausi, 2002;Bogdanov et al, 2005;Maillard et al, 2005;Yu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

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Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

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Section: Calculation Of Texture Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

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“…Although it is not exactly sea classification, recently, a lot of effort has been put in sea-ice classification in regions like the Baltic Sea. Some of this research has been presented in [16][17][18]. However, a thorough study of classification focused only on the sea surface has yet to be done.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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“…These features are minimum gray level, maximum gray level, mean gray level, median gray level, standard deviation of gray levels, coefficient of variation, gray level skew-ness, gray level kurtosis, gray level energy, gray level entropy, and mode gray level [23,24].…”

Section: First Order Statistics (Fos)mentioning

confidence: 99%

“…The function f (k, l)is the intensity at pixel position (k, l) in the image of order (M × N). 22 different features are extracted in four different directions from this co-occurrence matrix where the pixels are counted in pairs [15,24,25]. A total of 22×4 = 88 features have been extracted from Gray Level CoOccurrence Matrix (GLCM).…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Tiwari¹,

Sachdeva²,

Ahuja³

et al. 2017

IJCIS

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The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI-class 1), Astrocytoma (AST-class 2), and Normal brain regions (NORM-class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

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Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Cited by 1,039 publications

References 58 publications

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

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