Optimization Of Polarimetric Contrast Enhancement Research Articles

Offshore Oil Platform Detection in Polarimetric SAR Images Using Level Set Segmentation of Limited Initial Region and Convolutional Neural Network

Offshore oil platforms are difficult to detect due to the complex sea state, the sparseness of target distribution, and the similarity of targets with ships. In this paper, we propose an oil platform detection method in polarimetric synthetic aperture radar (PolSAR) images using level set segmentation of a limited initial region and a convolutional neural network (CNN). Firstly, to reduce the interference of sea clutter, the offshore strong scattering targets were initially detected by the generalized optimization of polarimetric contrast enhancement (GOPCE) detector. Secondly, to accurately locate the contour of targets and eliminate false alarms, the coarse results were refined using an improved level set segmentation method. An algorithm for splitting and merging the smallest enclosing circle (SMSEC) was proposed to cover the coarse results and obtain the initial level set function. Finally, the LeNet-5 CNN model was used to classify the oil platforms and ships. Experimental results using multiple sets of polarimetric SAR data acquired by RADARSAT-2 show that the performance of the proposed method, including the detection rate, the false alarm rate, and the Intersection over Union (IOU) index between the extracted ROI and the ground truth, is better than the performance of a method that combines a GOPCE detector and a support vector machine classifier.

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: 50 km2); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: 113 km2); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: 5 km2). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

Novel Polarimetric Contrast Enhancement Method Based on Minimal Clutter to Signal Ratio Subspace

Enhancing the contrast of target and clutter is a crucial issue in synthetic aperture radar (SAR) image target detection. In this paper, we define a novel subspace, called minimal clutter-to-signal ratio (MCSR) subspace, which can minimize the clutter-to-signal ratio (CSR) by projecting the feature vector to the subspace. Based on MCSR, a novel polarimetric contrast enhancement method is proposed. The MCSR subspace is learned based on the commonly used polarimetric feature vectors extracted from the labeled training SAR image pixels. The feature vectors extracted form candidate SAR image pixels are projected to the MCSR subspace. By calculating the square norm of each transformed feature vector, an enhanced image can be obtained. It is demonstrated that the existing optimization of polarimetric contrast enhancement (OPCE) is a special case of the proposed method to some extent. Experimental results show that our method outperforms the traditional OPCE method on the RadarSat-2 SAR data.

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.

The Use of a Modified GOPCE Method for Forest and Nonforest Discrimination

This study focuses on the development and evaluation of the generalized optimization of polarimetric contrast enhancement (GOPCE) model to discriminate between forested and nonforested areas. The main objective is to investigate the performance of the GOPCE method for forest mapping and to assess the potential of different polarimetric parameters for forest representation. We make two modifications to the original GOPCE method. First, by comparing behaviors of different polarimetric parameters, the GOPCE model is modified. Then, linear discriminant analysis is employed for further optimization of the target contrast. Forest/nonforest discrimination results are demonstrated on L-band fully polarimetric ALOS-1/PALSAR data acquired over a pilot study area in northeastern Tasmania, Australia, where the main forest type is eucalypt forests. Two other forest classification approaches (i.e., support vector machine and canonical variate analysis) are also tested for comparison. The final results obtained from the modified GOPCE model with the generalized Fisher criterion can improve the forest/nonforest discrimination accuracy.

An Improved Generalized Optimization of Polarimetric Contrast Enhancement and Its Application to Ship Detection

The optimization of polarimetric contract enhancement (OPCE) is one of the important problems in radar polarimetry since it provides a substantial benefit for target enhancement. Considering different scattering mechanisms between the desired targets and the undesired targets, Yang et al. extended the OPCE model to the generalized OPCE (GOPCE) problem. Based on a modified GOPCE model and the linear discriminant analysis, a ship detector is proposed in this paper to improve the detection performance for polarimetric Synthetic Aperture Radar (SAR) imagery. In the proposed method, we modify the combination form of the three polarimetric parameters (i.e., the plane scattering similarity parameter, the diplane scattering similarity parameter and the Cloude entropy), then use an optimization function resembling the classical Fisher criterion to optimize the optimal polarization states corresponding to the radar received power and the fusion vector corresponding to the polarimetric parameters. The principle of the optimization detailed in this paper lies in maximizing the difference between the desired targets and sea clutter, and minimizing the clutter variance at the same time. RADARSAT-2 polarimetric SAR data acquired over Tanggu Port (Tianjin, China) on June 23, 2011 are used for validation. The experimental results show that the proposed method improves the contrast of the targets and sea clutter and meanwhile reduces the clutter variance. In comparison to another GOPCE based ship detector and the classical polarimetric whitening filter (PWF), the proposed method shows a better performance for weak targets. In addition, we also use the RADARSAT-2 data acquired over San-Francisco on April 9, 2008 to further demonstrate the improvement of this method for target contrast.

GOPCE-Based Approach to Ship Detection

A new approach is proposed for ship detection, based on the generalized optimization of polarimetric contrast enhancement (OPCE). The new approach consists of two steps. For the first step, the OPCE problem will be solved for obtaining optimal polarizations. In this way, the received powers of a sea area are suppressed. For the second step, we construct a function which not only contains the power term but also contains a term associated with the scattering mechanism. Three optimal coefficients will be obtained such that the variance of the function values of a sea clutter area is minimal. In this way, ships on the sea area are detected. Using two examples, we demonstrate the effectiveness of the proposed approach for ship detection.

Optimization of Polarimetric Contrast Enhancement Based on Fisher Criterion

The optimization of polarimetric contrast enhancement (OPCE) is a widely used method for maximizing the received power ratio of a desired target versus an undesired target (clutter). In this letter, a new model of the OPCE is proposed based on the Fisher criterion. By introducing the well known two-class problem of linear discriminant analysis (LDA), the proposed model is to enlarge the normalized distance of mean value between the target and the clutter. In addition, a cross-iterative numerical method is proposed for solving the optimization with a quadratic constraint. Experimental results with the polarimetric SAR (POLSAR) data demonstrate the effectiveness of the proposed method.

Iteration Based Polarimetric SAR Image Classification

In this paper, an iteration method is proposed for supervised polarimetric synthetic aperture radar (SAR) image classification. In this iterative approach, the optimization of polarimetric contrast enhancement (OPCE) is employed for enlarging the distance between the mean values of two kinds of targets and the Fisher method is employed for reducing the variances of two distributions. Using the proposed approach, polarimetric SAR image can be classified only after a few iterations. For comparison, the authors also use the maximum likelihood (ML) classifier for classification, based on the complex Wishart distribution. The classification results of a NASA/JPL AIRSAR L-band image over San Francisco demonstrate the effectiveness of the proposed approach. DOI: 10.2529/PIERS061004064550

Polarimetric SAR image classification by using generalized optimization of polarimetric contrast enhancement

In this letter, a generalized optimization of polarimetric contrast enhancement (GOPCE) is employed for supervised polarimetric synthetic aperture radar (SAR) image classification. The GOPCE is the extension of optimization of polarimetric contrast enhancement (OPCE), and it includes three optimal coefficients associated with the Cloude entropy and two special similarity parameters in addition to the optimal polarization states. Using the GOPCE, the authors propose an approach to supervised classification. For comparison, the authors also use the maximum likelihood (ML) classifier for classification, based on the complex Wishart distribution. The classification results of a NASA/JPL AIRSAR L‐band image over San Francisco demonstrate the effectiveness of the proposed approach.

Generalized Optimization of Polarimetric Contrast Enhancement

A generalized optimization of polarimetric contrast enhancement (GOPCE) is proposed in this letter. For this problem, it is not only necessary to find the optimal polarization states such that the received power ratio of a desired target and clutter is maximal, but also necessary to find three optimal coefficients such that the ratio of two factors associated with the desired target and clutter is maximal, where both the factors consist of three parameters, i.e., the Cloude entropy and two special similarity parameters. The optimal coefficients of the GOPCE are obtained by solving an eigenvalue problem. Using an example, we demonstrate that the GOPCE can be employed for detecting roads in a forest area by using polarimetric synthetic aperture radar data.

Optimal polarization for contrast enhancement in polarimetric synthetic aperture radar using a genetic algorithm

Polarimetric scattering information provided by fully polarimetric synthetic aperture radar (SAR) is useful for discriminating between different terrain covers. Conventionally, eigenanalysis is used to synthesize a polarimetric matched filter (PMF) that maximizes the contrast between the features of interest, but this involves many algebraic operations, and only one contrast ratio for any two classes can be obtained at one time. This paper describes the application of a genetic algorithm to the contrast enhancement. When it is used, all the contrast ratios characterizing the best discrimination for any two of all possible classes can be obtained simultaneously, providing a new and more convenient way to solve the problem of the optimization of polarimetric contrast enhancement. Two independent sets of L-band fully polarimetric SAR images are used as test data; the results show that the proposed method is very effective and promising.