The effective monitoring of low-altitude slow small (LSS) targets represented by unmanned aerial vehicle (UAV) is a great challenge in the field of security in recent years. Most of the existing infrared (IR) small target algorithms focus on high-altitude target detection. However, the low-altitude background is complex and changeable, and high-intensity suspected targets exist widely. Existing methods usually cause high false alarm or failure detection for LSS targets. In this letter, we propose a novel spatiotemporal saliency method for LSS IR targets in image sequences. First, spatial variance saliency mapping and temporal gray saliency mapping are calculated in spatial domain and temporal domain, respectively. Then, the fusion saliency map is obtained by fusing the spatial saliency map and temporal saliency map. Finally, the target is extracted by a simple adaptive threshold segmentation. The proposed method is verified in five low-altitude IR image sequences. Experimental results demonstrate that the proposed method can achieve better detection performance than the existing state-of-the-art methods for LSS targets.

The field of remote sensing change detection (RSCD) has seen significant advancements recently, focusing on the precise identification and analysis of temporal changes in remote sensing images. Existing deep learning-based RSCD methods primarily rely on concatenation or subtraction to integrate features of bi-temporal images and reconstruct change features through a feature pyramid network decoding architecture. However, these methods face challenges related to inadequate spatio-temporal change representation and insufficient aggregation of multilevel semantic information, resulting in pseudo-changes and poor completeness of detected change objects. In this paper, we propose an innovative RSCD framework via spatio-temporal feature fusion and guide aggregation (STFF-GA) to address the aforementioned challenges. The architecture of this network comprises two key components: the STFF module and the GA module. The STFF module is designed as a low-parameter and low-computation structure, effectively enhancing the representation of spatio-temporal change information through split, interaction, and fusion strategies. The GA module uses deep feature guidance (DFG) mapping as prior information to guide the aggregation of multi-level semantic information, thereby correcting the positional information of change objects and filtering out pseudo-changes and other noise interference. Additionally, it utilizes convolution kernels of various scales to extract fine-grained features, facilitating the complete reconstruction of change objects. Extensive experiments conducted on three benchmark change detection datasets demonstrate that the proposed STFF-GA consistently outperforms other state-of-the-art detectors. The code is available at https://github.com/NjustHGWei/STFF-GA.

Infrared (IR) small target detection in the complex background is an important but challenging research hotspot in the field of target detection. The existing methods usually cause high false alarms in the complex background and fail to make full use of the complete information of the image. In this article, a novel IR small target detection model that combines facet derivative-based multidirectional edge awareness with spatial–temporal tensor (FDMDEA-STT) is presented. First, we construct an STT model (STTM) to transform the target detection problem into a low-rank and sparse tensor optimization problem based on the prior information of the target and background in the spatial–temporal domain. Then, based on the facet derivative, we define a multidirectional edge awareness mapping and fuse it into the STTM as sparse prior information. Finally, an effective algorithm based on the alternating direction method of multipliers (ADMM) is designed to solve the above model. The effectiveness of the proposed method is verified on eight real IR image sequences. Experimental results demonstrate that the proposed method has better detection performance than the existing state-of-the-art methods.

Fast and stable detection of dim and small infrared (IR) targets in complex backgrounds has important practical significance for IR search and tracking system. The existing small IR target detection methods usually fail or cause high probability of false alarm in the highly heterogeneous and complex backgrounds. Continuous motion of a target relative to the background is important information regarding detection. In this paper, A low rank and sparse decomposition method based on greedy bilateral factorization is proposed for IR dim and small target detection. Firstly, by analyzing the complex structure information of IR image sequences, the target is regarded as an independent sparse motion structure and an efficient optimization algorithm is designed. Secondly, the greedy bilateral factorization strategy is adopted to approximate the low-rank part of the algorithm, which significantly accelerates the efficiency of algorithm. Extensive experiments demonstrate that the proposed method has better detection performance than the existing methods. The proposed method can still detect targets quickly and stably especially in complex scenes with weak signal-to-noise ratio.

Detecting small infrared (IR) targets against low-altitude complex background is always a challenge for IR search and tracking (IRST) system due to limited small target characteristics, the moving background caused by camera motion, and extremely cluttered backgrounds. The existing methods usually cause high false alarm or do not work against the chaotic low-altitude complex background. In this article, a novel spatial–temporal tensor model with saliency filter regularization (STTM-SFR) is developed to detect small IR targets. First, the small target detection task is transformed into a sparse and low-rank tensor optimization problem using the spatial–temporal prior knowledge of background and target. The construction of the holistic STTM can retain the complete spatial–temporal information of the original IR image sequence. Then, the SFR term limited between background and foreground aims to promote target saliency learning. That is to say, the SFR term can avoid the offset approximation of the low-rank tensor, so as to recover a clean target image from the original IR tensor. Finally, an effective alternating direction method of multipliers (ADMM) algorithm framework is designed to solve the proposed STTM-SFR model. The effectiveness and robustness of the STTM-SFR model are verified in six real IR scenes. Experimental results show that our method outperforms other baseline methods. Moreover, the proposed STTM-SFR method is more robust than the existing state-of-the-art STTMs against low-altitude moving backgrounds.

Existing approaches still face issues such as the lack of spatial-temporal information and target prior information, as well as low detection efficiency when dealing with small infrared (IR) target detection tasks under heterogeneous backgrounds. To address the aforementioned issues, this article presents a low rank tensor approximation with saliency prior (LRTA-SP) approach, where the holistic spatiotemporal tensor model (STTM) is constructed by combining spatiotemporal related prior information of IR videos with target saliency prior information. Firstly, benefiting by the subspace optimization theory, the target and the background can be separated into a sparse and a low rank component, respectively. Crucially, the proposed LRTA-SP optimization approach updates the background tensor on a tangent space to accelerate the above optimization process, greatly reducing the computational complexity of low rank projection while improving the detection efficiency of our model. Also, applying a multi-rank constrain in low rank regularization term helps to adaptively preserve important information in the frequency domain. Furthermore, a prior weight tensor containing target saliency information is provided in sparse regularization term to preserve contextual information during the optimization process. Finally, an alternating projection-based algorithm framework is designed to robustly separate sparse targets and low rank backgrounds. The effectiveness and superiority, especially the detection efficiency, of the proposed LRTA-SP technology to similar detection technologies are validated on six real IR videos under various scenarios.