Lena Chang

A region-based GLRT detection of oil spills in SAR images

In the study, we propose a fast region-based method for the detection of oil spills in SAR images. The proposed method combines the image segmentation technique and conventional detection theory to improve the accuracy of oil spills detection. From the image statistical characteristics, we first segment the image into regions by using moment preserving method. Then, to get a more integrated segmentation result, we adopt N-nearest-neighbor rule to merge the image regions according to their spatial correlation. Performing the split and merge procedure, we can partition the image into oil-polluted and sea reflection regions, respectively. Based on the segmentation results, we build data models of oil spills and approximate them by using normal distributions. Employing the built oil spills model and the generalized likelihood ratio test (GLRT) detection theory, we derive a closed form solution for oil spills detection. Our proposed method possesses a smaller variance and can reduce the confusion interval in decision. Moreover, we adopt the sample average of image region to reduce the computation complexity. The false alarm rate and oil spills detection probability of the proposed method are derived theoretically. Under the criterion of constant false alarm ratio (CFAR), we determine the threshold of the decision rule automatically. Simulation results performed on ERS2-SAR images have demonstrated the efficiency of the proposed approach.

A Parallel Simulated Annealing Approach to Band Selection for High-Dimensional Remote Sensing Images

In this paper a parallel band selection approach, referred to as parallel simulated annealing band selection (PSABS), is presented for high-dimensional remote sensing images. The approach is based on the simulated annealing band selection (SABS) scheme which is originally designed to group highly correlated hyperspectral bands into a smaller subset of modules regardless of the original order in terms of wavelengths. SABS selects sets of correlated hyperspectral bands based on simulated annealing (SA) algorithm and utilizes the inherent separability of different classes to reduce dimensionality. In order to be effective, the proposed PSABS is introduced to improve the computational performance by using parallel computing technique. It allows multiple Markov chains (MMC) to be traced simultaneously and fully utilizes the parallelism of SABS to create a set of SABS modules on each parallel node. Two parallel implementations, namely the message passing interface (MPI) cluster-based library and the open multi-processing (OpenMP) multicore-based application programming interface, are applied to three different MMC techniques: non-interacting MMC, periodic exchange MMC and asynchronous MMC for evaluation. The effectiveness of the proposed PSABS is evaluated by NASA MODIS/ASTER (MASTER) airborne simulator data sets and airborne synthetic aperture radar (SAR) images for land cover classification during the Pacrim II campaign in the experiments. The results demonstrated that the MMC techniques of PSABS can significantly improve the computational performance and provide a more reliable quality of solution compared to the original SABS method.

Band selection for hyperspectral images based on parallel particle swarm optimization schemes

Greedy modular eigenspaces (GME) has been developed for the band selection of hyperspectral images (HSI). GME attempts to greedily select uncorrelated feature sets from HSI. Unfortunately, GME is hard to find the optimal set by greedy operations except by exhaustive iterations. The long execution time has been the major drawback in practice. Accordingly, finding an optimal (or near-optimal) solution is very expensive. In this study we present a novel parallel mechanism, referred to as parallel particle swarm optimization (PPSO) band selection, to overcome this disadvantage. It makes use of a new particle swarm optimization scheme, a well-known method to solve the optimization problems, to develop an effective parallel feature extraction for HSI. The proposed PPSO improves the computational speed by using parallel computing techniques which include the compute unified device architecture (CUDA) of graphics processor unit (GPU), the message passing interface (MPI) and the open multi-processing (OpenMP) applications. These parallel implementations can fully utilize the significant parallelism of proposed PPSO to create a set of near-optimal GME modules on each parallel node. The experimental results demonstrated that PPSO can significantly improve the computational loads and provide a more reliable quality of solution compared to GME. The effectiveness of the proposed PPSO is evaluated by MODIS/ASTER airborne simulator (MASTER) HSI for band selection during the Pacrim II campaign.

Simulated annealing band selection approach for hyperspectral imagery

In hyperspectral imagery, greedy modular eigenspace (GME) was developed by clustering highly correlated bands into a smaller subset based on the greedy algorithm. Unfortunately, GME is hard to find the optimal set by greedy scheme except by exhaustive iteration. The long execution time has been the major drawback in practice. Accordingly, finding the optimal (or near-optimal) solution is very expensive. Instead of adopting the band-subset-selection paradigm underlying this approach, we introduce a simulated annealing band selection (SABS) approach, which takes sets of non-correlated bands for high-dimensional remote sensing images based on a heuristic optimization algorithm, to overcome this disadvantage. It utilizes the inherent separability of different classes embedded in high-dimensional data sets to reduce dimensionality and formulate the optimal or near-optimal GME feature. Our proposed SABS scheme has a number of merits. Unlike traditional principal component analysis, it avoids the bias problems that arise from transforming the information into linear combinations of bands. SABS can not only speed up the procedure to simultaneously select the most significant features according to the simulated annealing optimization scheme to find GME sets, but also further extend the convergence abilities in the solution space based on simulated annealing method to reach the global optimal or near-optimal solution and escape from local minima. The effectiveness of the proposed SABS is evaluated by NASA MODIS/ASTER (MASTER) airborne simulator data sets and airborne synthetic aperture radar images for land cover classification during the Pacrim II campaign. The performance of our proposed SABS is validated by supervised k-nearest neighbor classifier. The experimental results show that SABS is an effective technique of band subset selection and can be used as an alternative to the existing dimensionality reduction method.

Group and Region Based Parallel Compression Method Using Signal Subspace Projection and Band Clustering for Hyperspectral Imagery

In this study, a novel group and region based parallel compression approach is proposed for hyperspectral imagery. The proposed approach contains two algorithms, which are clustering signal subspace projection (CSSP) and the maximum correlation band clustering (MCBC). The CSSP first divides the image into proper regions by transforming the high dimensional image data into one dimensional projection length. The MCBC partitions the spectral bands into several groups according to their associated band correlation for each image region. The image data with high degree correlations in spatial/spectral domains are then gathered in groups. Then, the grouped image data is further compressed by Principal Components Analysis (PCA)-based spectral/spatial hyper-spectral image compression techniques. Furthermore, to accelerate the computing efficiency, we present a parallel architecture of the proposed compression approach by using parallel cluster computing techniques. Simulation results performed on AVIRIS images have shown that the proposed group and region based approach performs better than standard 3D hyperspectral image compression. Moreover, the proposed approach achieves better computation efficiency than the direct combination of PCA and JPEG2000 under the same compression ratio.

A Parallel Simulated Annealing Approach to Band Selection for Hyperspectral Imagery

In this paper we present a parallel band selection approach, referred to as parallel simulated annealing band selection (PSABS), for hyperspectral imagery. The approach is based on the simulated annealing band selection (SABS) scheme. The SABS algorithm is originally designed to group highly correlated hyperspectral bands into a smaller subset of band modules regardless of the original order in terms of wavelengths. SABS selects sets of non-correlated hyperspectral bands based on simulated annealing (SA) algorithm and utilizes the inherent separability of different classes in hyperspectral images to reduce dimensionality. In order to be effective, the proposed PSABS is introduced to improve the computational speed by using parallel computing techniques. It allows multiple Markov chains (MMC) to be traced simultaneously and fully utilizes the significant parallelism embedded in SABS to create a set of PSABS modules on each parallel node implemented by the message passing interface (MPI) cluster-based library and the open multi-processing (OpenMP) multicore-based application programming interface. The effectiveness of the proposed PSABS is evaluated by MODIS/ASTER airborne simulator (MASTER) hyperspectral images for hyperspectral band selection during the PACRIM II campaign. The experimental results demonstrated that PSABS can significantly improve the computational loads and provide a more reliable quality of solution compared to the original SABS method.

Multispectral image compression using eigen-region-based segmentation

In the study, we present an effective segmentation technique for multispectral image compression. This technique fully exploits the spectral and spatial correlation in the data. The original image is first divided into some proper eigen-regions according to the local terrain characteristics of the image. Then, each region image is transformed by the corresponding KL transformation function and results in an eigen-region image for further compression. Simulation tests performed on Landsat TM images have demonstrated that the proposed compression scheme is suitable for multispectral image.

A Combined Signal Subspace Projection and Partial Filtering Approach to Target Detection for Hyperspectral images

In the study, a novel signal subspace projection (SSP) approach is first proposed to detect and extract target signatures in unknown background. The weights of SSP are equivalent to optimal weights given by Wiener-Hopf equations. To further reduce the computation complexity in SSP, we implement the SSP-based classifier by an adaptive filter combined with some partial filters, called CSSPPF. In CSSPPF, we partition the image into several groups according to their associated band correlation. By the way, we transfer the design of a large fully filter into several small partial filter designing. Simulation results performed on AVIRIS images have demonstrated the efficiency of the proposed approaches.

An eigen-index technique for content-based retrieval of satellite image databases

In the study, we present a novel indexing technique for the retrieval of satellite image databases. The approach partitions the original image into some eigen-regions according to local terrain characteristics. And index for each eigen-region, called eigen-index, is extracted by the principal eigenvector of region. Using the eigen-index separation between the specified cover type of query image and that of each object image in the database, the access of archive could be facilitated. To show the performance of the proposed approach, a PHP-based interface agent using the eigen-index for content-based access to a test archive of SPOT images is available over the internet [http://dsp.mmd.ntou.edu.tw]. Through this agent, a user can browse ten best-matched candidates for each query.

An efficient hierarchical hyperspectral image classification using binary quaternion-moment-preserving thresholding technique

In the study, we propose a novel unsupervised classification technique for hyperspectral images, which consists of two algorithms, referred to as the maximum correlation band clustering (MCBC) and hierarchical binary quaternion-moment-preserving (BQMP) thresholding technique. By the MCBC, we partition the bands into groups and transfer the high-dimensional image data into low-dimensional image features. Afterwards, the hierarchical BQMP approach partitions the feature image into proper regions according to the spectral characteristics. Simulation results performed on AVIRIS images have demonstrated the efficiency of the proposed approaches.