Spatiotemporal image fusion is considered as a promising way to provide Earth observations with both high spatial resolution and frequent coverage, and recently, learning-based solutions have been receiving broad attention. However, these algorithms treating spatiotemporal fusion as a single image super-resolution problem, generally suffers from the significant spatial information loss in coarse images, due to the large upscaling factors in real applications. To address this issue, in this paper, we exploit temporal information in fine image sequences and solve the spatiotemporal fusion problem with a two-stream convolutional neural network called StfNet. The novelty of this paper is twofold. First, considering the temporal dependence among image sequences, we incorporate the fine image acquired at the neighboring date to super-resolve the coarse image at the prediction date. In this way, our network predicts a fine image not only from the structural similarity between coarse and fine image pairs but also by exploiting abundant texture information in the available neighboring fine images. Second, instead of estimating each output fine image independently, we consider the temporal relations among time-series images and formulate a temporal constraint. This temporal constraint aiming to guarantee the uniqueness of the fusion result and encourages temporal consistent predictions in learning and thus leads to more realistic final results. We evaluate the performance of the StfNet using two actual data sets of Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and both visual and quantitative evaluations demonstrate that our algorithm achieves state-of-the-art performance.

Spatiotemporal fusion is important in providing high spatial resolution earth observations with a dense time series, and recently, learning-based fusion methods have been attracting broad interest. These algorithms project image patches onto a feature space with the enforcement of a simple mapping to predict the fine resolution patches from the corresponding coarse ones. However, the sophisticated projection, e.g., sparse representation, is always computationally complex and difficult to be implemented on large patches, which cannot grasp enough local structural information in the coarse patches. To address these issues, a novel spatiotemporal fusion method is proposed in this letter, using a powerful learning technique, i.e., extreme learning machine (ELM). Unlike traditional approaches, we devote to learning a mapping function on difference images directly, rather than the sophisticated feature representation followed by a simple mapping. Characterized by good generalization performance and fast speed, the ELM is employed to achieve accurate and fast fine patches prediction. The proposed algorithm is evaluated by five actual data sets of Landsat enhanced thematic mapper plus-moderate resolution imaging spectroradiometer acquisitions and experimental results show that our method obtains better fusion results while achieving much greater speed.

Correlation filter (CF) based trackers have aroused increasing attentions in visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal interference during tracking procedure, the trained tracker should not only have the ability to judge the current state when failure occurs, but also to resist the model drift caused by challenging distractions. To this end, we present a State-aware Anti-drift Tracker (SAT) in this paper, which jointly model the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be efficiently solved using Alternative Direction Method of Multipliers and fully carried out in Fourier domain. Furthermore, a Kurtosis-based updating scheme is advocated to reveal the tracking condition as well as guarantee a high-confidence template updating. Extensive experiments are conducted on OTB-100 and UAV-20L datasets to compare the SAT tracker with other relevant state-of-the-art methods. Both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness of the proposed work.

With the wide vision and high flexibility, unmanned aerial vehicle (UAV) has been widely used into object tracking in recent years. However, its limited computing capability poses a great challenges to tracking algorithms. On the other hand, Discriminative Correlation Filter (DCF) based trackers have attracted great attention due to their computational efficiency and superior accuracy. Many studies introduce spatial and temporal regularization into the DCF framework to achieve a more robust appearance model and further enhance the tracking performance. However, such algorithms generally set fixed spatial or temporal regularization parameters, which lack flexibility and adaptability under cluttered and challenging scenarios. To tackle such issue, in this letter, we propose a novel DCF tracking model by introducing dynamic spatial regularization weight, which encourage the filter focuses on more reliable region during training stage. Furthermore, our method could optimize the spatial and temporal regularization weight simultaneously using Alternative Direction Method of Multiplies (ADMM) technique method, where each sub-problem has closed-form solution. Through the joint optimization, our tracker could not only suppress the potential distractors but also construct robust target appearance on the basis of reliable historical information. Experiments on two UAV benchmarks have demonstrated that our tracker performs favorably against other state-of-the-art algorithms.

Discriminative correlation filters (DCFs) have recently achieved competitive performance in visual tracking benchmarks. However, most of the existing DCF trackers only consider the spatial features of the target and could hardly benefit from the inter-frame and historical information, which may degrade the tracking performance when occlusion and deformation occurs. To tackle the above issues, in this letter, by introducing the temporal constrain into the DCF tracker, we advocate our Spatial-Temporal Context-Aware tracker (STCAT). Through jointly modeling the spatial context and historical target information, our tracker could not only adapt the appearance change but also maintain a relatively stable filter due to the small target variation between inter-frames. Furthermore, we show that the proposed objective formula could be directly solved using ADMM technique with low computational cost. Experiments on the large-scale benchmark demonstrate that the proposed trackers perform favorably against other state-of-the-art methods.

In real-time applications, a fast and robust visual tracker should generally have the following important properties: 1) feature representation of an object that is not only efficient but also has a good discriminative capability and 2) appearance modeling which can quickly adapt to the variations of foreground and backgrounds. However, most of the existing tracking algorithms cannot achieve satisfactory performance in both of the two aspects. To address this issue, in this paper, we advocate a novel and efficient visual tracker by exploiting the excellent feature learning and classification capabilities of an emerging learning technique, that is, extreme learning machine (ELM). The contributions of the proposed work are as follows: 1) motivated by the simplicity and learning ability of the ELM autoencoder (ELM-AE), an ELM-AE-based feature extraction model is presented, and this model can provide a compact and discriminative representation of the inputs efficiently and 2) due to the fast learning speed of an ELM classifier, an ELM-based appearance model is developed for feature classification, and is able to rapidly distinguish the object of interest from its surroundings. In addition, in order to cope with the visual changes of the target and its backgrounds, the online sequential ELM is used to incrementally update the appearance model. Plenty of experiments on challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.