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Recently, tracking with unmanned aerial vehicle (UAVs) platforms has played significant roles in Earth observation tasks. However, target occlusion remains a challenging factor during the continuous tracking procedure. In particular, incomplete local appearance features can mislead the tracking network to produce inaccurate size and position estimations when the target is occluded. Furthermore, the tracking network lacks sufficient occlusion supervision information, which may lead to template degradation during template updating. To address these challenges, in this article, we design an occlusion-aware tracker with local-global features modeling, which contains two key components, namely the feature intrinsic association module (FIAM) and the feature verification module (FVM). Specifically, the FIAM divides the local features into blocks and utilizes the transformer network to explore the relative relationships among each subblock, which supplements the damaged local target features and assists the modeling for global target features. In addition, the FVM establishes a correlation measurement network between the target and the template. To precisely evaluate the occlusion status, masked samples with occlusion exceeding 50% are selected as negative samples for independent training, which ensures the purity of the target template. Qualitative and quantitative experiments are conducted on publicly available datasets, including UAV20 L, UAV123, and LaSOT. Qualitative and quantitative experiments have demonstrated the effectiveness of the proposed tracking algorithm over the other state-of-the-art trackers in occlusion scenarios.

As a significant research direction in remote sensing fields, unmanned aerial vehicles (UAVs) tracking has achieved rapid development in recent years. However, due to limited power and computation resources on aerial platforms, the tracking methods deployed on UAVs usually require high computational efficiency and performance. In addition, various challenges (i.e., similar object, background clutter, and occlusion) have inevitably occurred during the UAV tracking phase. Therefore, considering the above issues comprehensively, this article proposes a dynamic aberrance-repressed temporal regularized correlation filter (CF) to achieve stable tracking in UAV remote sensing videos. First, we have introduced the aberrance-repressed temporal regularizations into the discriminative CF framework. Second, a novel objective loss function is constructed to adjust the strength of each regularization for training the filter. Then, a new judgment mechanism based on the response variation is exploited to reflect the response fluctuation and applied to tune parameters of both regularizations. Finally, comprehensive experiments are done on three different UAV benchmarks, i.e., UAV123@10fps, UAVDT, and VisDrone2018, to verify the performance of our tracker and have demonstrated that our tracker achieves superior performance against other total 25 state-of-the-art trackers while reaching $\sim$35 FPS on a single CPU.

As the number of space targets increases year by year, space situational awareness plays an important role in satellite security. To keep satellite secure, we use space target monitoring, removal, and avoidance to complete space situational awareness. These tasks usually use space target classification algorithms, and the algorithms should be compared fairly with a space target dataset. But at present, real data collection is difficult, and the existing simulation datasets lack some space models. These incomplete space target datasets limit the sustainable development of this field. Therefore, this article proposes a space target dataset to make up for the shortcomings of the existing space target dataset. 1) More realistic. Our dataset considers the real distribution and operation of space targets and counts their real number and orbital position. We simulate the real space target environment to ensure that the dataset can be migrated to practical applications. 2) More diverse. Our dataset includes 11 classes of satellites and 35 classes of space debris. In addition, we use a large number of lighting angles and shooting orbits for each model. 3) More reliable. In order to reduce artificial data bias, we count the attributes of the dataset to analyze the consistent distribution of attributes such as the resolution and contrast of space target images in different classes. Then, we made some targeted modifications. In addition, we propose a feature-based quantitative evaluation method for reliability, which calculates the distribution among different classes in the space target dataset.

Hyperspectral imagery (HSI) is an emerging remote sensing technology to discriminate different remote sensing objects. However, the HSI spatial resolution is relatively low due to the trade-off in restricted physical hardware and various imaging conditions, restricting the subsequent object detection applications. At present, the single hyperspectral super resolution (SHSR) strategy has encountered the bottleneck on more precise details extraction, and the fusion hyperspectral image super resolution (FHSR) strategy must need extra RGB/multispectral information, which is not suitable for general HSI usage. Also, both types of current strategies focus less on the multiple degradation causes of low spatial resolution. In this article, a step forward in designing a novel framework of multiple frame splicing strategy to greatly improve the SHSR quality, and applying multiple HSI degradation models to better fit the real degradation circumstance. Specifically, the framework is an end-to-end super resolution (SR) network that supersedes a single up-sampling module and removes complex attention residual model due to the same size of multiple splicing low-resolution input samples with high-resolution outputs. The effective framework will alleviate the vague at higher multiples, and accelerate the training convergence. Based on this framework, multiple degradation low-resolution samples can be simultaneously combined to fit better for the blind SR result. Concretely, the degradation focus on the blur, noise, compression, and their combinations to simulate the real degradation. Experimental results on three different hyperspectral datasets demonstrate that the proposed multiple frame splicing and degradation model (MFSDM) algorithm can significantly enhance the details in the recovered high-resolution hyperspectral images, and outperforms the state-of-the-art SHSR methods.

Drought poses a significant constraint on rice production, and, in this study, we have discovered a novel drought-sensitive mutant, designated as dsm3, arising from the progenies of indica rice variety Zhonghui8015 treated with ethyl methane sulphonate (EMS). Under drought stress conditions, dsm3 exhibited characteristic withered leaf tips, accompanied by increased levels of malondialdehyde (MDA) and H2O2, a reduced net photosynthetic rate (Pn), and decreased activity of peroxidase (POD) and superoxide dismutase (SOD). Genetic analysis revealed that the withered leaf tip phenotype was governed by a single recessive gene, designated as Osdsm3. To begin with, Osdsm3 was initially mapped to the short arm of chromosome 1 through a cross involving dsm3 and 02428. Subsequently, utilizing a population of 2591 F2 individuals, we narrowed down the location of Osdsm3 to a 78 Kb interval, encompassing 13 open reading frames (ORFs). Sequencing analysis unveiled a mutation (1275G → A) in the exon of the candidate gene (LOC_Os01g10680), leading to premature translation termination. Moreover, a quantitative RT-PCR assay demonstrated a high expression of OsDSM3 in the panicle and sheath, with a significant upregulation of drought-stress-related genes under drought conditions. Phylogenetic analyses indicated that Osdsm3 shares evolutionary homology with UNE1, an intracellular transport protein found in Arabidopsis thaliana. Subcellular studies further confirmed that OsDSM3 resides in the cytoplasm. In conclusion, the forthcoming cloning of Osdsm3 holds promise for delving deeper into the molecular mechanisms governing rice drought resistance.

Recently, the improvement of detection performance always relies on deeper convolutional layers and complex convolutional structures in remote sensing images, which significantly increases the storage space and computational complexity of the detector. Although previous work has designed various novel lightweight convolutions, when these convolutional structures are applied to remote sensing detection tasks, the inconsistency between features and targets as well as between features and tasks in the detection architecture is often ignored: (1) The features extracted by convolution sliding in a fixed direction make it difficult to effectively model targets with arbitrary direction distribution, which leads to the detector needing more parameters to encode direction information and the network parameters being highly redundant; (2) The detector shares features from the backbone, but the classification task requires rotation-invariant features while the regression task requires rotation-sensitive features. This inconsistency in the task can lead to inefficient convolutional structures. Therefore, this paper proposed a detector that uses the Feature Decoupling for Lightweight Oriented Object Detection (FDLO-Det). Specifically, we constructed a rotational separable convolution that extracts rotational equivariant features while significantly compressing network parameters and computational complexity through highly shared parameters. Next, we introduced an orthogonal polarization transformation module that decomposes rotational equivariant features in both horizontal and vertical orthogonal directions, and used polarization functions to filter out the required features for classification and regression tasks, effectively improving detector performance. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD show that the proposed detector can achieve the best performance and achieve an effective balance between computational complexity and detection accuracy.

Aircraft recognition in high-resolution remote sensing images has rapidly progressed with the advance of convolutional neural networks (CNNs). However, the previous CNN-based methods may not work well for recognizing aircraft in low-resolution remote sensing images because the blurred aircraft in these images offer insufficient details to distinguish them from similar types of targets. An intuitive solution is to introduce superresolution preprocessing. However, conventional superresolution methods mainly focus on reconstructing natural images with detailed texture rather than constructing a high-resolution object with strong discriminative information for the recognition task. To address these problems, we propose a unified framework for joint superresolution and aircraft recognition (Joint-SRARNet) that tries to improve the recognition performance by generating discriminative, high-resolution aircraft from low-resolution remote sensing images. Technically, this network integrates superresolution and recognition tasks into the generative adversarial network (GAN) framework through a joint loss function. The generator is constructed as a joint superresolution and refining subnetwork that can upsample small blurred images into high-resolution ones and restore high-frequency information. In the discriminator, we introduce a new classification loss function that forces the discriminator to distinguish between real and fake images while recognizing the type of aircraft. In addition, the classification loss function is back-propagated to the generator to obtain high-resolution images with discriminative information for easier recognition. Extensive experiments on the challenging multitype aircraft of remote sensing images (MTARSI) dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a small blurred image and significant improvement in the recognition performance. To our knowledge, this is the first work on joint superresolution and aircraft recognition tasks.

Hyperspectral images (HSIs) with abundant spectral information are generally susceptible to various types of noise, such as Gaussian noise and stripe noise. Recently, a few quality-based selection algorithms have been proposed to remove noise bands from HSIs. However, these methods suffer from an inability to discriminate the mixed-noise bands of HSIs and are sensitive to image content variation and luminance changes. Here, we develop a mixed-noise band selection framework that can separate the Gaussian and stripe noise bands from HSIs effectively. We first improve tensor decomposition to reconstruct the mixed-noise components and low-rank components, which reduces the influence of image content and luminance changes. Spectral smoothness constraints and unidirectional total variation are incorporated into the decomposition model to enhance the separation performance for Gaussian and stripe noise. Then, different statistical features, including Weibull and histogram of oriented gradient (HOG) features, are applied to extract the robust parameters from mixed-noise components. More importantly, an extreme learning machine (ELM) is trained to predict the noise bands. The ELM has an extremely fast learning speed and tends to achieve better performance than other networks. Finally, by aggregating all these strategies, our methods can select the mixed-noise bands efficiently. The experimental results on both synthetic and real noise HSIs indicate that the proposed method outperforms the state-of-the-art methods.

In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA).

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods.

Multi-exposure image fusion is becoming increasingly influential in enhancing the quality of experience of consumer electronics. However, until now few works have been conducted on the performance evaluation of multi-exposure image fusion, especially colorful multi-exposure image fusion. Conventional quality assessment methods for multi-exposure image fusion mainly focus on grayscale information, while ignoring the color components, which also convey vital visual information. We propose an objective method for the quality assessment of colored multi-exposure image fusion based on image saturation, together with texture and structure similarities, which are able to measure the perceived color, texture, and structure information of fused images. The final image quality is predicted using an extreme learning machine with texture, structure, and saturation similarities as image features. Experimental results for a public multi-exposure image fusion database show that the proposed model can accurately predict colored multi-exposure image fusion image quality and correlates well with human perception. Compared with state-of-the-art image quality assessment models for image fusion, the proposed metric has better evaluation performance.

The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance.

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.

This paper presents design and implementation of digital receiver based on large point fast Fourier transform (FFT) suitable for electronic warfare (EW) applications. When implementing the FFT algorithm on field-programmable gate array (FPGA) platforms, the primary goal is to maximize throughput and minimize area. This algorithm adopts two-dimension, parallel and pipeline stream mode and implements the reconfiguration of FFT's points. Moreover, a double-sequence-separation FFT algorithm has been implemented in order to achieve faster real time processing in broadband digital receivers. The performance of the hardware implementation on the FPGA platforms of broadband digital receivers has been analyzed in depth. It reaches the requirement of high-speed digital signal processing, and reveals the designing this kind of digital signal processing systems on FPGA platforms. Keywords: digital receivers, field programmable gate array (FPGA), fast Fourier transform (FFT), large point reconfigured, signal processing system.