Subspace-based multitask learning framework for hyperspectral imagery classification

Multimedia Tools and Applications - Subspace-based models have been widely applied for hyperspectral imagery applications, especially for classification. The main principle of these methods is...     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

EFAST Method for Global Sensitivity Analysisof Remote Sensing Model’s Parameters

Sensitivity analysis for parameters of remote sensing physical models is a prerequisite for inversion.The EFAST(Extended Fourier Amplitude Sensitivity Test)as a global sensitivity analysis method,can analyze not only a single parameter’s sensitivity but also the coupling effects among parameters.It is usually applied to analyse parameters’ sensitivity of the high-dimensional nonlinear models.In this paper,the SAIL model is taken as an example,the EFAST method and the field measured data of winter wheat in Shunyi district in 2001 were applied to analyze the model parameters’ sensitivity throughout the growing season and in different growth stages respectively.The results are compared with those of the USM (Uncertainty and Sensitivity Matrix) method.The results show that either the EFAST or the USM method for parameters’ sensitivity analysis of the SAIL model is feasible;but the EFAST method,which takes into account of the coupling effects among all the parameters and the analysis result is global,compared to the USM method,is more objective and comprehensive.     

Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data

Although the combined use of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data in land-cover classification has been widely adopted, the majority of such use of Landsat and MODIS data is done at the pixel level or feature input level in land-cover classification. We propose in this research a new method to make integrated use of different satellite data by adaptively weighted decision-level fusion. Training and validation samples were collected independently. Training samples were obtained from 329 regions and validation samples from 439 randomly distributed single-point positions. A Support Vector Machine (SVM) classifier was applied to the Landsat 8 data for classification and probability estimation. A Random Forests (RF) classifier was applied to the MODIS time-series data for probability estimation. Weight values were computed based on decision credibility, and reliability values were computed based on data quality. Three decision fusion procedures were performed. In the first procedure, decisions obtained from a Landsat 8 pixel and its corresponding MODIS pixel were fused for improvements (FUSION1). In the second, decisions obtained from the spatial neighbours of the Landsat 8 pixel were added to FUSION1 (FUSION2). In the third, decision fusion only among the Landsat 8 pixel and its spatial neighbours was performed (FUSION3) for comparison. Overall accuracies for the results with Landsat data only, FUSION1, FUSION2, and FUSION3 are 74.0%, 79.3%, 80.6%, and 75.6%, respectively. As a comparison, we also experimented on the use of Landsat and MODIS data by concatenating their features directly. Two classifiers, SVM and RF, were trained and validated on the concatenated features. The overall accuracies were 72.9% and 75.4%, respectively. Results show that the proposed method can utilize information selectively, so that considerable improvements can be obtained and fewer errors introduced. Moreover, it can be easily extended to handle more than two types of data source.     

Adaptive–interpolative binarization with stroke preservation for restoration of faint characters in degraded documents

A novel technique for binarization with stroke preservation of faint characters in degraded documents is proposed. It works in a multi-scale framework with an adaptive–interpolative thresholding technique. Instead of computing a global threshold value, it computes the local threshold values for a small set of grid points by observing the intensity pattern of the pixels lying in the concerned grid cells. Estimated thresholds are used, in turn, to compute the threshold values of all the remaining pixels using a fast-yet-efficient interpolation procedure. To handle noises in degraded images, this grid-based adaptive thresholding is applied in successively reducing scales to obtain the near-optimal binarization as a set of connected components. After a post-processing meant for stroke preservation with these connected components, we get the final output. Exhaustive experimentation and comparison with other existing methods have been successfully carried out with benchmark datasets and also with our own datasets.     

Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

An all-season sample database for improving land-cover mapping of Africa with two classification schemes

High-quality training and validation samples are critical components of land-cover and land-use mapping tasks in remote sensing. For large area mapping it is much more difficult to build such sample sets due to the huge amount of work involved in sample collection and image processing. As more and more satellite data become available, a new trend emerges in land-cover mapping that takes advantage of images acquired beyond the greenest season. This has created the need for constructing sample sets that can be used in classifying images of multiple seasons. On the other hand, seasonal land-cover information is also becoming a new demand in land and climate change studies. Here we produce the first training and validation data sets with seasonal labels in order to support the production of seasonal land-cover data for entire Africa. Nonetheless, for the first time, two classification systems were created for the same set of samples. We adapted the finer resolution observation and monitoring of global land cover (FROM-GLC) and the Food and Agriculture Organization (FAO) Land Cover Classification System legends. Locations of training-sample units of FROM-GLC were repurposed here. Then we designed a process to enlarge the training-sample units to increase the density of samples in the feature space of spectral characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series and Landsat imagery. Finally, we obtained 15,799 training-sample units and 7430 validation-sample units. The land-cover type at each point was recorded at the time of maximum greenness in addition to four seasons in a year. Nearly half of the sample units were also suitable for 500 m resolution MODIS data. We analysed the representativeness of the training and validation sets and then provided some suggestions about their use in improving classification accuracies of Africa.     

Mathematical modelling and parameter identification of a stainless steel annealing furnace

A new, comprehensive mathematical model of continuous annealing furnaces is developed, under consideration of both the radiative and convective heat transfer of the furnace components. Based on measured normal operating data from an industrial stainless steel plant, parameter identification is basically carried out using a nonlinear least-squares optimization algorithm for the whole annealing furnace, to estimate optimal values of uncertain parameters, such as emissivities. Due to the complexity of the model, a sequential approach for parameter identification is proposed and implemented, i.e. the parameter set is divided into different subsets, and the parameter estimation is carried out sequentially in several steps and iterations. The performance of the model with the estimated parameters is then evaluated on a different test data set. It is shown that the obtained model can predict temperature evolutions along the furnace in good agreement to measured data, under both steady-state and transient conditions. The presented model is suitable for controller design and process optimization.     

Transparent three-phase Byzantine fault tolerance for parallel and distributed simulations

A parallel and distributed simulation (federation) is composed of a number of simulation components (federates). Since the federates may be developed by different participants and executed on different platforms, they are subject to Byzantine failures. Moreover, the failure may propagate in the federation, resulting in epidemic effect. In this article, a three-phase (i.e., detection, location, and recovery) Byzantine Fault Tolerance (BFT) mechanism is proposed based on a transparent middleware approach. The replication, checkpointing and message logging techniques are integrated in the mechanism for the purpose of enhancing simulation performance and reducing fault tolerance cost. In addition, mechanisms are provided to remove the epidemic effects of Byzantine failures. Our experiments have verified the correctness of the three-phase BFT mechanism and illustrated its high efficiency and good scalability. For some simulation executions, the BFT mechanism may even achieve performance enhancement and Byzantine fault tolerance simultaneously.