This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

Fast steering mirror (FSM) plays a crucial role in stabilization of the line‐of‐sight (LOS) and phase shift compensation. The control accuracy of the FSM is affected by various disturbances especially the vibration in the aviation environment. Traditional anti‐disturbance methods, such as disturbance observer (DOB), have a little effect of suppressing disturbance in FSM. But it also brings some problem, such as increasing mass and amplifying high frequency noise. To solve these problems, an anti‐disturbance strategy based on adaptive robust control (ARC) was proposed. And it will not amplify the high‐frequency noise which is inevitable in DOB. Experimental results show that, using adaptive robust controller, the steady‐state error of the FSM decreased 4.8 times compared to simple PID control and 1.9 times compared to DOB+PID control in the simulated vibration environment.

Visual tracking methods have been successful in recent years. Correlation filter (CF)-based methods significantly advanced state-of-the-art tracking. The advancement in CF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, there would be trouble if the tracker indiscriminately learned samples. Particularly, when the target is severely occluded or out-of-view, the tracker will continuously learn the wrong information, resulting target loss in the following frames. In this study, aiming to avoid incorrect training when occlusions occur, we propose a regional color histogram-based occlusion estimating agency (RCHBOEA), which estimates the occlusion level and then instructs, based on the result, the tracker to work in one of two modes: normal or lost. In normal mode, an occlusion level-based self-adopting learning rate is used for tracker training. In lost mode, the tracker pauses its training and conducts a search and recapture strategy on a wider searching area. Our method can easily complement CF-based trackers. In our experiments, we employed four CF-based trackers as a baseline: discriminative CFs (DCF), kernelized CFs (KCF), background-aware CFs (BACF), and efficient convolution operators for tracking: hand-crafted feature version (ECO_HC). We performed extensive experiments on the standard benchmarks: VIVID, OTB50, and OTB100. The results demonstrated that combined with RCHBOEA, the trackers achieved a remarkable improvement.

Accurate semantic segmentation of remote sensing (RS) imagery faces significant challenges due to the presence of small targets, ambiguous features, and complex scenes. Conventional convolutional networks, although computationally efficient, do not adequately perceive the global context. In contrast, transformers excel in global context awareness but incur a higher computational cost. To bridge this gap, we introduce BotWaveNet, an innovative semantic segmentation framework designed for RS images. This framework is inspired by the global context proficiency exhibited by BoTNet. BotWaveNet reconfigures the fourth layer of ResNet by replacing conventional convolutional operations with a spatial attention mechanism (SAM), thus establishing an encoder–decoder structure that integrates attention mechanisms within deep network architectures. The SAM module is specifically engineered to enhance edge delineation by promoting pixel interconnectivity, thereby capturing global context more effectively. Furthermore, we have designed a feature fusion module (wavelet attention module) that fine-tunes the extraction of channel features, optimizing adaptability for both synthetic aperture radar and optical images. In an effort to reduce the computational demands associated with MaxPooling, we have incorporated a wavelet transform module. This module serves as an innovative replacement for traditional convolutional pooling methods, aiming to maintain efficiency while enhancing performance. The efficacy of BotWaveNet has been substantiated through its significant performance enhancements observed in the Gaofen Challenge and the Inria Aerial Image Labeling Dataset.

Weakly supervised semantic segmentation (WSSS) aims to segment objects without a heavy burden of dense annotations. Pseudo-masks serve as supervisory information for training segmentation models, which is crucial to the performance of segmentation models. However, the generated pseudo-masks contain significant noisy labels, which leads to poor performance of the segmentation models trained on these pseudo-masks. Few studies address this issue, as these noisy labels remain inevitable even after the pseudo-masks are improved. In this paper, we propose an uncertainty-weight transform module to mitigate the impact of noisy labels on model performance. It is noteworthy that our approach is not aimed at eliminating noisy labels but rather enhancing the robustness of the model to noisy labels. The proposed method adopts a frequency-based approach to estimate pixel uncertainty. Moreover, the uncertainty of pixels is transformed into loss weights through a set of well-designed functions. After dynamically assigning weights, the model allocates attention to each pixel in a significantly differentiated manner. Meanwhile, the impact of noisy labels on model performance is weakened. Experiments validate the effectiveness of the proposed method, achieving state-of-the-art results of 69.3% on PASCAL VOC 2012 and 39.3% on MS COCO 2014, respectively.