UAV object tracking by correlation filter with adaptive appearance model

With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach

Robust Correlation Tracking for UAV Videos via Feature Fusion and Saliency Proposals

Following the growing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on object tracking using videos recorded from UAVs. However, tracking from UAV videos poses many challenges due to platform motion, including background clutter, occlusion, and illumination variation. This paper tackles these challenges by proposing a correlation filter-based tracker with feature fusion and saliency proposals. First, we integrate multiple feature types such as dimensionality-reduced color name (CN) and histograms of oriented gradient (HOG) features to improve the performance of correlation filters for UAV videos. Yet, a fused feature acting as a multivector descriptor cannot be directly used in prior correlation filters. Therefore, a fused feature correlation filter is proposed that can directly convolve with a multivector descriptor, in order to obtain a single-channel response that indicates the location of an object. Furthermore, we introduce saliency proposals as re-detector to reduce background interference caused by occlusion or any distracter. Finally, an adaptive template-update strategy according to saliency information is utilized to alleviate possible model drifts. Systematic comparative evaluations performed on two popular UAV datasets show the effectiveness of the proposed approach

Deep learning for remote sensing image classification:A survey

Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel?wise and scene?wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL?based RS methods is also provided. Finally, the challenges and potential directions for further research are discussedpublishersversionPeer reviewe

Semantic-Aware Real-Time Correlation Tracking Framework for UAV Videos

Discriminative correlation filter (DCF) has contributed tremendously to address the problem of object tracking benefitting from its high computational efficiency. However, it has suffered from performance degradation in unmanned aerial vehicle (UAV) tracking. This article presents a novel semantic-aware real-time correlation tracking framework (SARCT) for UAV videos to enhance the performance of DCF trackers without incurring excessive computing cost. Specifically, SARCT first constructs an additional detection module to generate ROI proposals and to filter any response regarding the target irrelevant area. Then, a novel semantic segmentation module based on semantic template generation and semantic coefficient prediction is further introduced to capture semantic information, which can provide precise ROI mask, thereby effectively suppressing background interference in the ROI proposals. By sharing features and specific network layers for object detection and semantic segmentation, SARCT reduces parameter redundancy to attain sufficient speed for real-time applications. Systematic experiments are conducted on three typical aerial datasets in order to evaluate the performance of the proposed SARCT. The results demonstrate that SARCT is able to improve the accuracy of conventional DCF-based trackers significantly, outperforming state-of-the-art deep trackers

Bridging Sensor Gaps via Single-Direction Tuning for Hyperspectral Image Classification

Recently, some researchers started exploring the use of ViTs in tackling HSI classification and achieved remarkable results. However, the training of ViT models requires a considerable number of training samples, while hyperspectral data, due to its high annotation costs, typically has a relatively small number of training samples. This contradiction has not been effectively addressed. In this paper, aiming to solve this problem, we propose the single-direction tuning (SDT) strategy, which serves as a bridge, allowing us to leverage existing labeled HSI datasets even RGB datasets to enhance the performance on new HSI datasets with limited samples. The proposed SDT inherits the idea of prompt tuning, aiming to reuse pre-trained models with minimal modifications for adaptation to new tasks. But unlike prompt tuning, SDT is custom-designed to accommodate the characteristics of HSIs. The proposed SDT utilizes a parallel architecture, an asynchronous cold-hot gradient update strategy, and unidirectional interaction. It aims to fully harness the potent representation learning capabilities derived from training on heterologous, even cross-modal datasets. In addition, we also introduce a novel Triplet-structured transformer (Tri-Former), where spectral attention and spatial attention modules are merged in parallel to construct the token mixing component for reducing computation cost and a 3D convolution-based channel mixer module is integrated to enhance stability and keep structure information. Comparison experiments conducted on three representative HSI datasets captured by different sensors demonstrate the proposed Tri-Former achieves better performance compared to several state-of-the-art methods. Homologous, heterologous and cross-modal tuning experiments verified the effectiveness of the proposed SDT

3D-ANAS v2: Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been a hot topic for decides, as Hyperspectral image has rich spatial and spectral information, providing strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms are proposed for HSI classification, which further improve the accuracy of HSI classification to a new level. In this paper, we revisit the search space designed in previous HSI classification NAS methods and propose a novel hybrid search space, where 3D convolution, 2D spatial convolution and 2D spectral convolution are employed. Compared search space proposed in previous works, the serach space proposed in this paper is more aligned with characteristic of HSI data that is HSIs have a relatively low spatial resolution and an extremely high spectral resolution. In addition, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed ConvNet to adding global information to local region focused features learned by ConvNet. We carry out comparison experiments on three public HSI datasets which have different spectral characteristics to evaluate the proposed method. Experimental results show that the proposed method achieves much better performance than comparison approaches, and both adopting the proposed hybrid search space and grafting transformer module improves classification accuracy. Especially on the most recently captured dataset Houston University, overall accuracy is improved by up to nearly 6 percentage points. Code will be available at: https://github.com/xmm/3D-ANAS-V2.Comment: 15 pages, 10 figure

Preparation of c-TiO₂ films; Preparation of TiO₂NSs films; SEM Images; J-V characteristic; Tables from Enhanced photovoltaic properties of perovskite solar cells by TiO₂ homogeneous hybrid structure

The c-TiO₂ films were deposited onto the clean FTO by chemical bath deposition (CBD) method with 0.06 M TiCl₄ aqueous solution at 70 ℃ for 30 min. The c-TiO₂ films were annealed at 450 ℃ for 30 min in ambient conditions.; TiO₂NSs films were fabricated by a hydrothermal method; Fig. S1 Top-view images of TiO₂NSs on FTO/c-TiO₂ prepared at 170 ℃ for (a) 1 h; (b) 2 h; (c) 3 h; (d) 4 h, the scale bar is 500nm. Fig. S3 (a) Top-view image of 7C TiO₂NPs on FTO/c-TiO₂; (b) the enlarged view of TiO₂NPs; (c) cross-sectional SEM image of 7C TiO₂NPs on FTO/c-TiO₂.;Fig. S2 J-V characteristic of the lead iodide perovskite solar cells based on TiO₂NSs films of different reaction time. Fig. S4 J-V characteristic of the lead iodide perovskite solar cells based on 3h TiO₂NSs/7CNPs and 7C NPs films.; Table. S1 Photovoltaic Device Parameters of the TiO₂NSs/CH₃NH₃PbI₃ Solar Cells. Table. S2 Photovoltaic Device Parameters of the FTO/c-TiO₂/TiO₂NSs/NPs/CH₃NH₃PbI₃/HTM/Ag and FTO/c-TiO₂/TiO₂NPs/CH₃NH₃PbI₃/HTM/Ag Solar Cells