Soil can be used as a damage indicator of landslides and flooding, which expose soil from vegetation canopy. It can also be used as an indirect indicator of illegal tunnel digging activity. This letter presents a sparsity-based approach to soil detection using multispectral satellite images, where both original and synthetic bands have been used. Spatial and spectral information has then been jointly used in soil detection. Extensive experiments clearly demonstrated the feasibility of our approach.

This paper summarizes a new investigation of applying advanced pansharpening algorithms to enhance the images of the left imager in the Mastcam onboard the Curiosity rover, which landed on Mars in 2012. The various instruments on the rover have already made great contributions in the understanding of Mars. The goal of our research is to generate both high spatial and high spectral image cube by using the left and right Mastcam imagers. Eleven algorithms have been investigated using five objective performance metrics. Subjective evaluations have also been conducted. The image enhancement results are encouraging.

In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.

In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification which exploits correlation as well as complementary information among homogeneous and heterogeneous sensors while simultaneously extracting the low-rank interference term. Specifically, we observe that incorporating the noise or interfered signal as a low-rank component is essential in a multi-sensor problem when multiple co-located sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to optimal solution is guaranteed. Extensive experiments are conducted on a real data set for a multi-sensor classification problem focusing on discriminating between human and animal footsteps. Results are compared with the conventional classifiers and existing sparsity-based representation methods to verify the effectiveness of our proposed models.

In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data. Previous attempts require explicit expression components, which are often unavailable and difficult to recover. Instead, our model exploits the low-rank property to subtract neutral faces from expressive facial frames as well as performs sparse representation on the expression components with group sparsity enforced. For the CK+ dataset, C-HiSLR on raw expressive faces performs as competitive as the Sparse Representation based Classification (SRC) applied on manually prepared emotions. Our C-HiSLR performs even better than SRC in terms of true positive rate.

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1 -norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of class-specific spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.

Radio-frequency interference (RFI) is the most common, and also the most challenging type of interference or noise source that has a direct impact on the performance of ultrawideband radar systems in various practical application settings. Existing techniques for RFI suppression either employ filtering (notching) which introduces other harmful side-effects such as side-lobe distortion and target-amplitude reduction or RFI modeling/estimation/tracking which requires complicated narrow-band modulation models or even direct RFI sniffing. In this paper, we propose a robust and adaptive technique for the separation and then suppression of RFI signals from ultra-wideband (UWB) radar data via modeling RFI as low-rank components in a joint optimization framework. More specifically, we advocate a joint sparse-and-low-rank recovery approach that simultaneously solves for (i) UWB radar signals as sparse representations with respect to a dictionary containing transmitted waveforms; and (ii) RFI signals as a low-rank structure. The proposed technique is completely adaptive with highly time-varying environments, and does not require any prior knowledge of the RFI sources (other than the low-rank assumption). Both simulated data and real-world data measured by the U.S. Army Research Laboratory (ARL) Ultra-Wideband (UWB) synthetic aperture radar (SAR) confirm that the proposed RFI separation/suppression technique successfully recovers UWB radar signals embedded in large-amplitude RFI signals.

Dictionary learning techniques have gained tremendous success in many classification problems. Inspired by the dirty model for multi-task regression problems, we proposed a novel method called group-structured dirty dictionary learning (GDDL) that incorporates the group structure (for each task) with the dirty model (across tasks) in the dictionary training process. Its benefits are two-fold: 1) the group structure enforces implicitly the label consistency needed between dictionary atoms and training data for classification; and 2) for each class, the dirty model separates the sparse coefficients into ones with shared support and unique support, with the first set being more discriminative. We use proximal operators and block coordinate decent to solve the optimization problem. GDDL has been shown to give state-of-art result on both synthetic simulation and two face recognition datasets.

In this paper, we propose a new video frame interpolation technique by a locally-adaptive robust principal component analysis (RPCA) with weight priors. The proposed algorithm relies on two main steps: 1. the pre-processing step initializes the new frame by a simplified motion-compensated frame interpolation and assigns each pixel a confident weight based on both the difference of motion estimation and local consistency; and 2. the refinement step updates the frame by a proposed weighted robust principal component analysis (WRPCA) algorithm. Experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms, both in visual quality and PSNR performance.

Sparse modeling has demonstrated its superior performances in many applications. Compared to optimization based approaches, Bayesian sparse modeling generally provides a more sparse result with a knowledge of confidence. Using the Spike and Slab priors, we propose the hierarchical sparse models for the scenario of single task and multitask - Hi-BCS and CHi-BCS. We draw the connections of these two methods to their optimization based counterparts and use expectation propagation for inference. The experiment results using synthetic and real data demonstrate that the performance of Hi-BCS and Chi-BCS are comparable or better than their optimization based counterparts.

The radar data to form synthetic aperture radar (SAR) imagery is normally transmitted and received by moving platforms like aircraft or vehicles. In many situations, the platforms move at high speed; which reduces the number of sampling records collected to the synthetic aperture, hence degrades the quality of the reconstructed SAR images. Therefore, it is necessary to develop an algorithm that is capable of increasing the temporal frequency rate of the received data. In this paper, we propose a novel technique to generate intermediate records from the existing ones by a locally-adaptive low-rank matrix recovery framework. The system first fills in the blank records using a bi-directional motion estimation scheme. The initialized aperture records are then refined by a robust low-rank matrix completion algorithm using the reference from neighborhood clean records. Experiments demonstrate that the proposed method provides comparative results when up-converting the aperture rate by a factor of two or four, both in mean square error of the raw SAR signal and PSNR performance of the recovered SAR images.

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, the Laplacian being the most common such choice (leading to l1-norm minimization). The recent seminal contribution by Wright et al. exploits the discriminative capability of sparse representations for image classification, specifically face recognition. Their approach employs the analytical framework of CS with class-specific dictionaries. Our contribution is a logical extension of these ideas into structured sparsity for classification. We use class-specific dictionaries in conjunction with discriminative class-specific priors, specifically the spike-and-slab prior widely applied in Bayesian regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes.

Video error concealment is a post-processing technique that conceals the errors in a decoded video sequence based on data available only at the decoder. Most of the current techniques adopt the approach that recovers the Motion Vector (MV) of a lost image block, uses that MV to look for data to fill in the blank then performs some refinements. We propose a method that does not rely on MV recovery, but essentially bases on sparse representation of image patches on local temporal dictionaries. Experiment results show a large improvement over Boundary Matching Algorithm (BMA), the standard method used in reference software for H.264 video codec.

This paper presents a novel video error concealment method, which is essentially a combination of non-local grouping of image patches and low-rank tensor approximation. The proposed method, though does not require the knowledge of Motion Vectors (MVs) as in traditional video concealment techniques such as Boundary Matching Algorithm (BMA) and its derivations, gives striking results in restoration of sequences, especially in the challenging cases when the error rate is high and/or key frames are not available. Our proposed framework can also be customized to deal with single images as well (for example, in image inpainting tasks).