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In the application of remote sensing SAR process in Guizhou karst mountainous region, the fragmented terrain and the complicated crops lead to the unsatisfactory precision, which is caused by using the grey value of SAR image simply in the classification. The original image was processed by the FROST filtering window with 7 × 7. After computing the different window size and direction of GLCM, the different texture characteristic values of GLCM were analyzed. The results show that step size of 1 and moving window with 5 × 5 of GLCM gets the optimum characteristic value. In addition, based on the four GLCM characteristic values of each landuse types, the correlation characteristic is fluctuating and easy to distinguish. To improve the precision of SAR image target recognition ability, as the image texture feature, the correlation characteristic will take part in the classification of the whole study area. But the range of the improvement needs further research.

This paper proposed a texture's feature of gray-level co-occurrence matrix (GLCM) as a calibrator of the sea wave height. Texture in Synthetic Aperture Radar (SAR) images represent the surface roughness of the sea or the wave height. As an archipelago country which has 70% sea of entire region, Indonesia needs and must have early warning system to provide transportation and fisherman safety circumvent the accident. Object of research are the P band full polarization Synthetic Aperture Radar (SAR) image acquired from coastal of East Kalimantan. These polarization are; horizontal-horizontal, horizontal-vertical, vertical-horizontal and vertical-vertical. Extraction of feature is done under observation angle 0° and adjacent pixels is 1. The results showed homogeneity is a consistent and toughest feature in horizontally-vertically polarization and vertical-vertical polarization and this feature can be used as a calibrator.

This paper explores different types of gray level co-occurrence matrix (GLCM) [2] texture features for automated detection of landslides on levees using remotely sensed Synthetic Aperture Radar (SAR). Two approaches of texture analysis are investigated: one based on a rubber band straightening transform (RBST) which has been used extensively in the past in the medical imaging community, and one based on the authors' developed approach of spiral straightening transform (SST). The transforms are used to project a circular region in the image to a rectangular representation where texture feature extraction can be applied. Straightforward linear discriminant analysis, for feature reduction and optimization, and maximum likelihood methods, for classification, are also utilized. The proposed system was tested on L-band SAR data with HH, HV, and VV polarizations collected from NASA's UAVSAR of the Mississippi River levee system between Vicksburg, MS and Clarksdale, MS, USA. The proposed approach is shown to detect all known levee landslides in the test area with a low number of false positives.

This paper proposes the classification framework based on the Bayesian theory with the single polarization multi-temporal synthetic aperture radar (SAR) and an optical data, and incorporates the proposed training sample selection (SS) methods. Within this framework, the combination with gray level co-occurrence matrix (GLCM)-based mean textural measure is investigated. The two procedures of the classification and proposed SS are united, where SS generates accurate and dispersed training samples. Extracted features from multi-temporal SAR data—namely, the average backscattering coefficient, backscatter temporal variability, and long-term coherence and reflectance values from optical data, are integrated with GLCM mean textural data in the framework. Classification results were generated by taking Osaka City, Japan, as the study area. The selected major classes were built-up areas, fields, woodlands, and water bodies. The most suitable data used for classification is the multi-temporal SAR and an optical data fusion with the mean textural, because of the supplement of different data used and the smoothing effect on the images of the texture. Moreover, the higher quality training samples obtained by using the combined SVM and NN-based SS method for training the Bayesian classifier generated the highest classification accuracies in all of tested cases.

This paper introduces the oil spill monitoring in SAR image by texture analysis and spectral information. In texture analysis, it discusses the parameters of texture extraction, and makes experiment. Then it determines the 17*17 as the windows size, 90 as the angle and 5 as distance. Through neural network classification, these parameters are suitable for oil spill monitoring. It can distinguish with oil and oil-like well. Its accuracy is satisfactory. These parameters are not only used for oil spill monitoring, but also provide a foundation for deeper SAR image classification.

The aim of this paper is to estimate freeze-thaw dates of a small lake using Synthetic Aperture Radar (SAR) images. Here, the estimation of freeze-thaw dates is restated as a classification problem wherein each SAR image in the time series is classified as an ‘ice’ or ‘no ice’ image based on the presence or absence of ice on the lake. We implement and compare different methods consisting of various feature extraction algorithms and nonparametric classification schemes. We obtain the best classification accuracy using histogram information from SAR images applied to a Random Forest classifier. Correct classification rate in the range of 90% is seen in this case and a standard median filter is used to estimate the freeze-thaw dates from the classifier output. We also extend our analysis to the use of texture features and local statistics in solving this problem.

Oil spill pollution is a major environmental threat for many countries in the world, which can cause serious damage to marine environment. Synthetic aperture radar (SAR) has become a valuable tool for marine oil spill monitoring, because of its all-weather and all-day capabilities. However, interpretation of marine SAR imagery is often ambiguous, and some other look-alike features often pose a fundamental challenge to the identification of oil spills and make the discrimination between oil spills and the look-alikes become a necessary procedure. In this paper, co-occurrence matrix method is employed to extract textural features of marine SAR image first, then these features are analyzed and optimized, and then support machine vector (SVM) method is used to identify oil spills in SAR images. Experiments on several SAR images show that method proposed in this paper can improve the detection and identification of oil spill in SAR images.

Texture features based on Haralick's co-occurrence matrix were compared for land cover and forestry classification purposes. According to these results, the best texture features were Angular Second Moment, Mean and Entropy, the worst Correlation and Standard Deviation. The best SAR-images were taken during wet snow or ground. Usually, the larger the window used to construct the co-occurrence matrix, the better the results. The suitable length of the spatial step depended on texture feature and classes. The directionless texture features performed usually well. The results of the performed classification experiment were disappointing.

Three techniques for generating texture statistics are examined: the gray-level co-occurrence matrix (GLCM), the gray-level difference vector (GLDV) and the neighboring gray-level dependence matrix (NGLDM). The objective of these statistical approaches is to translate visual texture properties into quantitative descriptors in a manner that they can be used to discriminate relevant land features using additional image processing techniques. These second-order statistical methods are used to generate texture features from C-HH and C-HV airborne synthetic aperture radar (SAR) data collected on July 10, 1990 over an agricultural area in southern Ontario Canada. Texture features generated from the GLCM, GLDV and NGLDM are classified individually using a k-nearest neighbor (k-NN) supervised classifier. The greatest classification improvement (/spl ap/20%) was observed with the mean and correlation texture features derived from the GLCM. However, the selection of a specific second-order statistical technique may not be critical, since similar classification improvements were observed for the GLCM, GLDV and NGLDM statistical techniques. The results reported here highlight the importance of texture processing to methods of classifying agricultural crops using SAR data.

Statistical, textural and Gabor features were extracted from integrated multitemporal multispectral TM data and ERS-1 SAR imagery for urban land use mapping. The computed features are first normalised using the SOM algorithm and then a decision tree algorithm is applied for feature selection. The classification procedure was carried out with a multilayer perceptron, trained with the resilient backpropagation algorithm. The authors' results demonstrate the potential of the proposed methodology.

A new method is proposed in this paper for coastline extraction which uses the SAR amplitude images, or if available, the interferometric coherence images. This method uses texture analysis based on textural multi-resolution (TMR) or geometrical multi-resolution (GMR), for a fine analysis of coastline. First order texture measures (TM) have been used. In TMR, a series of TM are generated at various textural scales from the original image. In GMR case, a wavelet transform produces a low-resolution approximation of the original image. Consequently, TM are also generated at a suitable low-resolution approximation. A selection of the suitable TM in one or another multi-resolution approach, leads to a good separation of sea and coastal area and reveals coastline. A detection of discontinuities allows then easy extraction of coastline. The obtained results are promising and show a good detection, and a good continuity of coastline. A proposed method has been tested on ERS SAR amplitude and coherence coastal region images of Cameroon, Mauritania and Guyana.

Using the nearly-global coverage of the ERS and JERS SAR satellites regional and global classification algorithms are now possible. Because they provide information at 2 distinct wavelengths, their combination has been useful for previous efforts towards such global classification algorithms. This paper extends that work with the use of various textural measures that allow the classification procedure to discriminate more classes (up to 11 so far) and with high accuracies (>85%). The texture features used were: grey-level c co-occurrence matrix measures, and a measure due to Ulaby that calculates a speckle-corrected standard deviation. Remarkably, the classification accuracies and number of classes is comparable to those obtained when using fully-polarimetric SAR data.