Copyright © 2010 Lamei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double- bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix (GCM), SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area (DK), Denmark. The preliminary result indicates that this method can classify most of the areas correctly.
Copyright © 2010 Tongyuan Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with diﬀerent classifiers. In this paper, we firstly evaluate and compare diﬀerent features for classifying polarimetric SAR imagery. Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but eﬃcient criterion. Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers. Experiments on ALOS PALSAR image validate the eﬀectiveness of the feature combination strategies and also show that ERCFs achieves competitive performance with other widely used classifiers while costing much less training and testing time.
PolSAR images are primarily based on Roy’s largest eigen value method or Wishart likelihood-ratio test (Wishart-LRT) . Because of the usage of windows, there is a tradeoff between edge locating accuracy and speckle suppression. Further transform based techniques have became popular. A two-scale line detection method was proposed in  using curvelet transform. Nonsubsampled contourlet transform (NSCT) based multiscale edge detection method was proposed in , and object detection based on bandelet transform dealt in  where bandelet applied on the spatial domain. Enhanced edge detector using span driven adaptive window put forwarded in . However it is more or less spatial technique with heterogeneous area adaptation. These methods have reduced the incidence of speckle noise as compared to traditional techniques. In this paper, we propose a multiscale detection based on bandelet transform technique and GCV thresholding. Edge enhancement of each element of the covariance matrix is done at different levels through bandelet domain and fused together for final edge image. The rest of the paper is organized as follows. Section 2 describes the conceptual review of PolSAR data representation ,, bandelet transform fundamentals and it advantages over wavelet transforms and GCV thresholding employed for the bandelet coefficient shrinkage for the speckle removal . Section III describes the proposed method of edge detection. Section IV details the results of proposed method using actual polarimetric data in comparison with the existing transform based methods. Comparison is done based on a handful of performance indices - expressed both in tabular form and spider plot.
In the experiment, we ﬁrst treat each pixel of the image as a vector made of three or nine elements, based on which the proposed Kernel KSVD algorithm performs decom- position and generates an over-complete dictionary of certain size. Then, we combine the sparse codes with spa- tial information using a three-level SPM to get the ﬁnal spatial pyramid features of the image. Finally, a simple linear SVM classiﬁer is used to test the classiﬁcation per- formance. The grid size of SPM is 1 × 1, 2 × 2 and 4 × 4. In each region of the spatial pyramid, the sparse codes are pooled together to form a new feature. There are three kinds of pooling methods, namely the max pooling (Max) , the square root of mean squared statistics (Sqrt), and
Polarimetric synthetic aperture radar (PolSAR) is a well- established multidimensional SAR technique based on acquiring earth’s surface information by means of using a pair of orthogonal polarizations for the transmitted and received electromagnetic fields [1, 2]. The object segmen- tation of PolSAR image plays an important role of PolSAR image understanding and analysis. In this paper, we focus on the problem of PolSAR image object segmentation. In literature, active contour model was well known to automatically recover the shape of objects from various types of images and provide a good detection of object boundaries in PolSAR images . In [4–6], several PolSAR image object segmentation methods based on the classical snake model  were proposed. But the classical snake model presents one limitation that topological changes which occur during the curve evolution are diﬃcult. Because the snake model discretizes a curve using a set of points, this representation is hard to describe the curve topological changes.
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6.2. Experiment 2. Experiment 2 was conducted to com- pare the performance of the wavelet-based fusion method with the multi-multi turbo iterative method. The data was collected at a test site in Oberpfaﬀenhofen, Ger- many. Figure 14(a) shows high-resolution optical data. Figure 14(b) shows a polarimetric SAR image of the same scene acquired by the E-SAR sensor of DLR . RGB values were assigned to HH, HV and VV. Figure 14(c) shows the results when the intensity components are fused using the traditional wavelet-based algorithm. Figure 14(d) shows the result of the proposed algorithm. To make it clear, Figure 14 presents two small cuts from the experiment images to be compared. Areas in orange and blue rectangles represent areas 1 and 2.
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Note that (16) implies that (26) is equivalent to (27) if issues of calibrator performance are ignored. However, gridded trihedrals, as used in Scheme 5 have the advantages of providing large beamwidth and giving average polarimetric noise (i.e., the coherent averaging of scattering vectors from different angular positions) less than -30 dB . On the negative side, they require accurate construction of the grid, and the microwave absorber layer is likely to be affected by rain. In contrast, scheme 6’s use of a trihedral and dihedral instead of gridded trihedrals brings the merits of simple construction and little effect from rain, but the narrow beamwidth of the dihedral causes orientation difficulties, and the dihedral suffers from high polarimetric noise due to pointing error .
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Mini- SAR data is primarily used to identify signatures suggesting the possibility of the presence of water ice (Fa, et al., 2013). Further, this data has been used to view various other geological features such as melt flows, crater ejecta blankets, secondary craters etc. (Saran, et al., 2012). Therefore, identification of distinct morphological features in shadowed as well as illuminated regions of lunar surfaces can be easily done using mini-SAR data (Spudis, et al., 2014). Digital image processing of mini-SAR data using m- χ decomposition technique was done to achieve single, double, and multiple/diffuse back scattering contributions to view tectonically derived morphological features in detail (Saran, et al., 2012). In the present study we are focussing on Mineralogical diversity and scattering characteristics of Byrgius Crater, King Crater, Taylor Crater, Descartes Crater and Maunder Crater.
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Vegetation and other natural features are considered to be azimuthally symmetric but certain fields may exhibit azimuthally asymmetric polarimetric behaviour due to orientation of row crops along the radar line of sight, tillage patterns, lodging (by strong winds) or harvesting patterns [2, 8]. As circular polarization enforces symmetric condition of natural media, it resulted into slightly poorer crop discrimination in certain cases and subjected to misclassification where asymmetric conditions exist. In the study area bare soil, built- up area and mustard classes were symmetrical, resulting into the classification accuracies similar in linear as well as circular quad- polarization data. Whereas for wheat (grown in rows) and gram (asymmetric condition at early stage due to effect of tillage), the asymmetric conditions resulted into lower classification rate in circular polarimetric data than the linear full-polarimetric data.
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In 1952, the first SAR system was successfully developed and confirmed by the non-coherent radar experiment at the University of Illinois, and the first SAR image was acquired in 1953. Since then, the United States has successively developed and launched several SAR satellites, in 1981. On November 12th, the "Colombia" mechanical SAR-A took off and successfully acquired a SAR image with surface penetration capability, which has aroused widespread concern in the international remote sensing community. Many countries have successively carried out research on spaceborne SAR. At the same time, SAR and multi-source image fusion have also received much attention. In 1992, in order to obtain more image information, the US Department of Defense established a fusion group to develop dozens of military fusion _________________________________________
The successful management of land use/land cover (LU/LC) planning and agricultural applications depends on continuous monitoring of LU/LC changes and crop growing conditions. Timely information about land surface is critical in the urban rural fringe areas in Southwestern Ontario, Canada, where rapid urban expansion has great influence on the agricultural production and the resultant economy. Frequent monitoring permits complete and accurate assessments of the impacts of urban development on the local and regional agriculture. Remote Sensing provides an efficient and effective tool for this purpose. The commonly available optical remote sensing data are not reliable for crop type identification and conditions monitoring during the growing seasons due to frequent overcast and rainy weather. SAR images provide an alternative data source to optical images. In addition, the newly available polarimetric SAR data contain full polarization information, and have greater potential compared with the traditional single polarization SAR data for the applications in LU/LC mapping and crop monitoring.
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The purpose of segmentation is to give meaning to an image and facilitate further analysis. In the particular case of the MSTAR dataset, two types of segmentation are observed. The segmentation can focus on the target only or on both the tar- get and its shadow. It is challenging to segment SAR images, as there are no sharp edges to delimit the target or the shadow from the background. The presence of noise with a high stan- dard deviation makes the choice of a direct threshold diffi- cult as either the target will not be entirely detected, or some background will be falsely detected. Most of the segmenta- tion methods already implemented try to isolate the target only  . After going through some pre-processing, the segmen- tation is done using thresholds. Some methods  enhance the precision of the method by applying an adapted threshold based on the contour of the previously found target. It is hard to evaluate and compare the segmentation results as there is no publicly available official ground truth. One manual seg-
The most likely source of error came from applying 2-D registration techniques to images taken from different oblique perspectives that contain significant topographic variation [Schott 2007]. Since the 2-D solution calculates everything relative to a common plane, values associated with extremely high or low points will be offset spatially from their true location after the registration process. The second major source of error was obscuration, which occurred when a target was visible in one image but not in the other due to a change in the sensor’s perspective. If no information about the target ever reached the focal plane for a given modality, then the required values were drawn from the nearest neighbor pixel. In the highly cluttered environment studied for this scenario, it was likely that the nearest neighbor and the pixel of interest were not composed of the same material—meaning a pure target pixel would appear as a mixed pixel in terms of spectral and polarimetric information, making it even harder to detect.
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The retrieval of subfield-scale crop biophysical variables using polarimetric SAR data is limited due to the speckle noise, the influence of soil on SAR backscatters in the early growing stage of crops, and the saturation phenomenon of polarimetric SAR data in the later growing stage. To make use of the high spatial resolution characteristics and the high temporal resolution characteristics of difference optical satellite sources, in Chapter 4, I developed a spatio-temporal vegetation index image fusion model (STVIFM) to blend MODIS and Landsat NDVI images for generating NDVI time series in a heterogeneous region. Similar to most spatio-temporal data fusion methods, the STVIFM assumes that the NDVI is additive. The NDVI change of each fine-resolution pixel is obtained by a disaggregation weighting system, which describes the contribution of each fine-resolution pixel to the total NDVI changes calculated from the coarse-resolution pixels. The weighting system considers the differences between fine-resolution and coarse-resolution pixel values on different dates. It also considers the variations of change rate at both spatial scale and temporal scale. The spatial variation of NDVI change of each fine-resolution pixel at any prediction date is calculated by incorporating the weights calculated based on one base fine-resolution image and the temporal NDVI change of the two fine-resolution images. These two elements are incorporated according to the land cover similarity between the prediction date and the two base dates. The STVIFM outperforms in NDVI prediction compared to the STARFM and ESTARFM when the land cover or NDVI changes are captured by the two pairs of fine- and coarse-resolution images. In addition, the STVIFM is more computationally efficient and more robust than the FSDAF. The STVIFM enhances the capability for generating both high spatial resolution and high temporal frequency NDVI time series in heterogeneous regions.
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Target detection in complex scenes, such as urban areas, airports or harbours, is a challenge in the area of synthetic aperture radar (SAR) image interpretation. Instead of a sin- gle scene, such as grassland, farmland or sea, the target de- tection performance in complex scenes is degraded by using conventional methods. In these complex scenes, the clutter produced by the background may be similar with the targets and are detected as false alarms. For example, the strong re- flections of urban building considerably affect vehicle detec- tion. Moreover, the echo waves from various backgrounds overlap and induce strong coherent speckles .
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snow coverage on the dome itself, and high topographic points. The July 29th images shows almost no snow over the diapir. Because of the reduced snow coverage, the later acquired July 28th 2016 RADARSAT-2 image is used to overlap the June 24th image to reduce interference from ice. .............................................................................................................. 33 Figure 19: Landsat-8 OLI image from September 19th, 2017 shows that snow or ice covered the acquisition areas of the RADARSAT-2 images from September 26th and 30th. Max-min stretch applied to the Landsat-8 image shows snow and ice in blueish-green. Despite the snow coverage, rock units in the right RADARSAT-2 image still shows distinctive CPR characteristics between the salt diaper, glacier and snow-covered rock units. ....................... 34 Figure 20: “True Colour” Landsat-8 OLI image (R: B4 (0.636-0.673), G: B3 (0.533-0.590), B: B2 (0.452-0.512)) of study area on Axel Heiberg Island. Visited salt diapirs are labelled. Red dots denote sites where samples for XRD analysis were taken (C: Colour Peak, I: Radar- rough Isachsen Formation, SF: South Fjord Diapir, St: Strand Diapir, Sz: Stolz Diapir, W: Wolf Diapir, WB: Whitsunday Bay Diapir). .......................................................................... 35 Figure 21: A helicopter traverse over the 5 km-diameter South Fjord Diapir showed that the dome was covered in snow in July 2017. Foreground (below white line) is the southern extent of South Fjord Diapir (Photo credit: Gordon Osinski 2017)........................................ 36 Figure 22: Many of the diapirs visited during the 2017 field season have crystalline structural components, in situ or as broken clasts. a) A broken clast of crystalline anhydrite with
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The proposed fusion method is compared with four fusion methods, including principal component analysis (PCA) , discrete wavelet transform (DWT) , non-sampled contourlet transform (NSCT)  and joint sparse representation (JSR) . PCA is a spectral transformation that has been widely employed for pan-sharpening. DWT is developed by setting different fusion rules for combining the coefficients of low frequency and high frequency sub-bands of the two source image separately in discrete wavelet domain, but it sometimes produces Gibbs effect in some degree. NSCT is a multiple resolution analysis method which introduces has shift invariant property, and has been applied for remote sensing image fusion in recent years. JSR is a novel image fusion method, by which source image is represented with the common and innovation sparse coefficients based on joint sparse representation. The experiment results are shown in Figure 9 and Figure 10.
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In this paper, we describe novel techniques for automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images. Specifically, we investigate two operating scenarios. In the first scenario, it is assumed that the polarimetric image pixels locally share the same covariance (homogeneous environment), whereas the second scenario considers polarimetric pixels with different power levels and the same covariance structure (heterogeneous environment). In the second case, we invoke the Principle of Invariance to get rid of the dependence on the power levels. For both scenarios, we formulate the classification problem in terms of multiple hypothesis tests which is addressed by applying the model order selection rules. The performance analysis is conducted on both simulated and measured data and demonstrates the effectiveness of the proposed approach.
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Among the incoherent target decompositions for polari- metric SAR (PolSAR) data, Freeman-Durden decompo- sition (FDD), which is based on a physical model, con- sists of three types of scattering mechanisms: volumetric scattering, double-bounce scattering, and surface scatter- ing . FDD has been used for PolSAR data processing, such as speckle filtering , image classification  and soil moisture estimation .
strongly dependent on the aspect angle, the simulation results were calculated by using the SAR geometry which poses dif- ferent directions for the incident and scattered waves. An im- portant point to note is that the difference between the scat- tering maxima of each target conforms with standard theory, thus confirming the validity of the calculations.