Speckle reduction for the forest mapping analysis of multi-temporal Radarsat-1 images

As the number of satellite-borne synthetic aperture radar (SAR) systems increases, both the availability and the length of multi-temporal (MT) sequences of SAR images have also increased. Previous research on MT SAR sequences suggests that they increase the classification accuracy for all applications over single date images. Yet the presence of speckle noise remains a problem and all images in the sequence must be speckle filtered before acceptable classification accuracy can be attained. Several speckle filters designed specifically for MT sequences have been reported in the literature. Filtering in the spatial domain, as is usually done, reduces the effective spatial resolution of the filtered image. MT speckle filters operate in both the spatial and temporal dimensions, thus the reduction in resolution is not likely to be as severe (although a comparison between MT and spatial filters has not been reported). While this advantage may be useful when extracting spatial features from the image sequence, it is not quite as apparent for classification applications. This research explores the relative performance of spatial and MT speckle filtering for a particular classification application: mapping boreal forest types. We report filter performance using the radiometric resolution as measured by the equivalent number of looks (NL), and classification performance as measured by the classification accuracy. We chose representative spatial and MT filters and found that spatial speckle filters offer the advantage of higher radiometric resolution and higher classification accuracy with lower algorithm complexity. Thus, we confirm that MT filtering offers no advantage for classification applications; spatial speckle filters yield higher overall performance.     

An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.     

Edge-detection-based filter for SAR speckle noise reduction

Some adaptive filters, such as the Kuan, Lee, minimum mean square error (MMSE) and Frost filters, have been tested on synthetic aperture radar (SAR) data without considering the level of homogeneity in the pixels. Therefore, they degrade the spatial resolution of images and smooth details, while also decreasing the speckle noise level. There are other filters, such as the enhanced Lee and gamma maximum a posteriori (MAP), that utilize the level of homogeneity, but they cannot adequately suppress speckle noise. In addition to these weaknesses, pixels surrounding a point scatterer are also treated as point scatterers due to inadequacy of the method based on evaluating the coefficient of variation for differentiating between them and the point scatterer. We have developed a new method based on the assessment of similarity of homogeneity levels in the image, incorporating edge-detection filters to identify meaningful features and an algorithm to filter the pixels surrounding point scatterers. This method, called the UNSW (University of New South Wales) adaptive filter (UAF), was compared to nine filters using different quantitative and qualitative methods. The results show the ability of the UAF to simultaneously reduce speckle and preserve details as well as its ability to filter more pixels. The effect of increasing the damping factor on speckle noise reduction performance has also been assessed using this method.     

极化合成孔径雷达相干斑抑制极化信息保持能力定量评估

针对采用极化特征图主观评估PolSAR相干斑抑制算法的极化信息保持能力存在一定的不足,提出了一种基于极化特征图相关系数的相干斑抑制效果评估方法.该方法实现了对PolSAR相干斑抑制算法极化信息保持能力的定量评估,能够更为精确地反映不同滤波器及滤波参数变化对PolSAR散射特性的影响.仿真数据和实测ESAR数据的相干斑抑制效果评估实验,验证了该方法的有效性.     

SAR图像去斑方法

合成孔径雷达图像固有的相干斑噪声严重降低了图像的可解译程度，影响了后续目标检测、分类和识别等应用。因此，SAR图像的相干斑抑制问题一直是SAR图像应用的重要课题之一。一个理想的去斑算法应该在平滑的同时保持图像的边缘等细节不受损失，目前存在各种各样的算法，但没有一种方法能够完美的满足这一要求。本文对SAR图像的相干斑抑制问题进行了全面系统的研究，分析了相干斑的形成原因，总结了目前存在的相干斑抑制算法的主要思路，介绍了具有代表性的算法，并对各种算法的性能进行了定性分析和比较，给出了去斑算法定量评估方法，展望了SAR图像相干斑抑制的发展方向。     

Wildfire monitoring using satellite images,ontologies and linked geospatial data

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications.     

Field of Particle Filters for Image Inpainting

We present a novel algorithm for solving the image inpainting problem based on a field of locally interacting particle filters. Image inpainting, also known as image completion, is concerned with the problem of filling image regions with new visually plausible data. In order to avoid the difficulty of solving the problem globally for the region to be inpainted, we introduce a field of local particle filters. The states of the particle filters are image patches. Global consistency is enforced by a Markov random field image model which connects neighbouring particle filters. The benefit of using locally interacting particle filters is that several competing hypotheses on inpainting solutions are kept active, allowing the method to provide globally consistent solutions on problems where other local methods may fail. We provide examples of applications of the developed method.     

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.     

Structure detection and statistical adaptive speckle filtering in SAR images

Current speckle filters attempt to restore the radar reflectivity using only the multiplicative speckle noise assumption. The best known filters, namely the Frost, Lee or Kuan niters are adaptive filters based on the local statistics, computed in a fixed square window. In this way, the speckle is reduced as a function of the heterogeneity measured by the local coefficient of variation. When the radar reflectivity undergoes significant variations due to the presence of strong scatterers or structural features (edges or lines) in the processing window, such speckle filtering is less effective. In this paper it is shown that the filtering process can be controlled both by the coefficient of variation and by various geometrical ratio detectors. Through shape adaptive windowing, these detectors allow the use of large window sizes for better speckle reduction while preserving spatial resolution and structural features. The backscattered intensity is modelled as

K-distributed within speckled targets and the filter uses a Bayesian approach which allows an explicit use of the multiplicative noise model and the radar reflectivity distribution.     

Toward self-describing and workflow integrated Earth system models: A coupled atmosphere-ocean modeling system application

The complexity of Earth system models and their applications is increasing as a consequence of scientific advances, user demand, and the ongoing development of computing platforms, storage systems and distributed high-resolution observation networks. Multi-component Earth system models need to be redesigned to make interactions among model components and other applications external to the modeling system easier. To that end, the common component interfaces of Earth system models can be redesigned to increase interoperability between models and other applications such as various web services, data portals and science gateways. The models can be made self-describing so that the many configuration, build options and inputs of a simulation can be recorded. In this paper, we present a coupled modeling system that includes the proposed methodology to create self-describing models with common model component interfaces. The designed coupled atmosphere-ocean modeling system is also integrated into a scientific workflow system to simplify routine modeling tasks and relationships between these tasks and to demonstrate the enhanced interoperability between different technologies and components. Later on, the work environment is tested using a realistic Earth system modeling application. As can be seen through this example, a layered design for collecting provenance and metadata has the added benefit of documenting a run in far greater detail than before. In this way, it facilitates exploration and understanding of simulations and leads to possible reproducibility. In addition to designing self-describing Earth system models, the regular modeling tasks are also simplified and automated by using a scientific workflow which provides meaningful abstractions for the model, computing environment and provenance/metadata collection mechanisms. Our aim here is to solve a specific instance of a complex model integration problem by using a framework and scientific workflow approach together. The reader may also note that the methods presented in this paper might be also generalized to other types of Earth system models, leading to improved ease of use and flexibility. The initial results also show that the coupled atmosphere-ocean model, which is controlled by the designed workflow environment, is able to reproduce the Mediterranean Sea surface temperature when it is compared with the used CCSM3 initial and boundary conditions.     

Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images

This paper describes a comparative evaluation of several speckle reduction and texture analysis techniques, with particular emphasis on their applicability to supervised land cover classification from SAR images. Issues related to suppression of speckle in a uniform area, preservation of edges, and texture preservation are pursued in these filters. Quality of texture features is measured by the relevancy, discriminative power and ease of computation of the features. The discriminative power of texture features is measured using the Jeffreys-Matusita distance and classification performance measured on a validation set independent from the classifier's training set. Classifiers investigated are maximum-likelihood, multi-layer perceptron (MLP) and radial basis function (RBF) neural networks. Classification accuracy is measured by KHAT statistic calculated from confusion matrices. Two SAR images of ERS-1 and E-SAR programme showing different land cover categories within the regions of Douala and Ngaoundere (Cameroon), and a bi-polarized Synthetic Aperture Radar (SAR) image from an agricultural station near the city of Altona (Canada) are used for analysis. Speckle suppression techniques based on the wavelet transform performs the best, followed by the modified K-nearest neighbours and the Lee's local statistic filters. Depending on the nature of the land cover types being classified, texture features derived from second- and third-order histogram performed the best, followed by first-order statistics and features derived using the grey-level difference vector method. Among all classifiers considered, the MLP and the RBF neural networks performed the best, achieving up to 94% overall accuracy for the E-SAR image of Douala, for example.     

Experiences in building a Grid-based platform to serve Earth observation training activities

Earth observation data processing and storing can be done nowadays only using distributed systems. Experiments dealing with a large amount of data are possible within the timeframe of a lesson and can give trainees the freedom to innovate. Following these trends and ideas, we have built a proof-of-the-concept platform, named GiSHEO, for Earth observation educational tasks. It uses Grid computing technologies to analyze and store remote sensing data, and combines them with eLearning facilities. This paper provides an overview of the GiSHEO's platform architecture and of its technical and innovative solutions.     

Experiences in building a Grid-based platform to serve Earth observation training activities

Earth observation data processing and storing can be done nowadays only using distributed systems. Experiments dealing with a large amount of data are possible within the timeframe of a lesson and can give trainees the freedom to innovate. Following these trends and ideas, we have built a proof-of-the-concept platform, named GiSHEO, for Earth observation educational tasks. It uses Grid computing technologies to analyze and store remote sensing data, and combines them with eLearning facilities. This paper provides an overview of the GiSHEO's platform architecture and of its technical and innovative solutions.     

Design and evaluation of human-machine communication for image information mining

Very large volumes of heterogenous data, like multimedia, Earth observation images, scientific and engineering measurements, for instance, are continuously generated and stored. A typical case is the field of Earth observation. The widespread availability of high resolution images does not only explore the volumes of data, but also brings order at magnitude in the image detail, thus enormously increasing the information content. However, today's concepts and technologies are still limited in communicating the information content to people for use in real life applications. In this paper, we overview a new concept for knowledge-driven image information mining (KIM) and both analyze and evaluate it from the perspective of human-machine communication. The KIM concept enables the information communication from a very large image repository to users via the Internet. The communication is at a semantic level of representation and is adapted to the user's conjecture.     

遥感数据融合方法分析与评价综述

论述了几种常用的基于像元级遥感影像融合方法的原理、特点、作用及限制条件，对各种相应的算法进行了分析和评价，归纳并阐明了遥感数据融合效果定量评价指标及其意义，展望了遥感数据融合方法的发展与应用前景。     

Training SVM email classifiers using very large imbalanced dataset

The Internet has been flooded with spam emails, and during the last decade there has been an increasing demand for reliable anti-spam email filters. The problem of filtering emails can be considered as a classification problem in the field of supervised learning. Theoretically, many mature technologies, for example, support vector machines (SVM), can be used to solve this problem. However, in real enterprise applications, the training data are typically collected via honeypots and thus are always of huge amounts and highly biased towards spam emails. This challenges both efficiency and effectiveness of conventional technologies. In this article, we propose an undersampling method to compress and balance the training set used for the conventional SVM classifier with minimal information loss. The key observation is that we can make a trade-off between training set size and information loss by carefully defining a similarity measure between data samples. Our experiments show that the SVM classifier provides a better performance by applying our compressing and balancing approach.     

Evaluation of speckle noise MAP filtering algorithms applied to SAR images

This work proposes new speckle reduction filters for multi-look, amplitude-detected Synthetic Aperture Radar (SAR) images based on the maximum a posteriori (MAP) approach and compares their performance. The new filters use an adaptive approach based on the one-dimensional k-means clustering algorithm over the variance ratio and also a region-growing procedure. The trade-off between the loss of radiometric resolution and edge preservation is evaluated in the filtered images. In order to obtain quantitative measures of the speckle reduction and of the edge blurring, we used some parameters such as the classical equivalent number of looks and the Hough transform. Experiments have been carried out with natural images corrupted with synthetic speckle noise following the Rayleigh and square root of gamma distributions and with real SAR images.     

Accurate Computation of Vocal Tract Filter Parameters Using a Hybrid Genetic Algorithm

This paper reports an original technique for accurately estimating the parameters of human vocal tract filters for vowels in English for speech processing applications such as voice recognition. In this paper, the vocal tract filter design problem is reformulated as a general nonlinear optimization problem and solved using a hybrid Genetic Algorithm (GA). The hybrid GA computes a rough estimate of the global minimum using GA and refines using computationally cheap local search. Issues that are of concern in digital filtering such as achieving stability and overcoming finite precision effects are addressed. The objective function for optimization used in this paper is formulated in terms of poles and zeros of the filters to avoid ill-conditioning and to take advantage of symmetries in the location of poles and zeros. Simulation results indicate that the approach presented in this paper provides a close fit in terms of mean square error between the experimental and designed filters.     

A toolbox for unsupervised change detection analysis

The analysis of multi-temporal remote-sensing images is one of the main applications in Earth’s observation and monitoring. In this paper, we present a Matlab toolbox for change detection analysis of optical multi-temporal remote-sensing data in which unsupervised approaches, iterative principal component analysis (ITPCA), and iteratively reweighted multivariate alteration detection (IR-MAD) are implemented and optimized. The optimization is represented by the implementation of novel pre- and post-processing strategies that aim to mitigate the side effects introduced by different acquisition conditions affecting change detection analysis. Special modules have been designed in order to decrease the required memory when large data sets are processed.     

Performance evaluation of slow sand filters using fuzzy rule-based modelling

The main objective of this study is to evaluate and predict the performance of slow sand filters used for wastewater treatment. The uncertainties in the control parameters and processes require fuzzy sets to be used when modelling system performance. Fuzzy logic if–then rules were used to build a model for the removal efficiency (total coliforms) of slow sand filters. The data were collected from three pilot-scale slow sand filters at the Alkhobar (Saudi Arabia) wastewater treatment plant. The removal efficiency of filters was modelled using three input control parameters—filtration rate, sand bed depth and grain size. Based on available data, fuzzy logic if–then rules were established. The fuzzy rule-based model was validated using experimental data of three case studies reported in the literature. The results were also compared with a multiple regression model. A possibilistic risk analysis was performed using optimal removal efficiency of the slow sand filters. The risk is estimated with respect to non-compliance of unrestricted agricultural reuse standards (100 total coliform/100 ml). In addition to slow sand filters, post- or pre-chlorination of wastewater is recommended to improve wastewater quality for conforming agricultural reuse standards.