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.

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.

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.

In the low-altitude urban background with heavy interference, especially in the face of corner interference with higher intensity than the target, infrared (IR) dim and small target is extremely lack of prior information (i.e., size, shape and contrast information). In such case, the existing detection methods usually suffer from high false alarm or even failure. To deal with this situation, we develop a novel spatial-temporal tensor model with tensor spectral k -support norm minimization (STTM-TSNM) for detecting IR dim and small target. Firstly, the spatial-temporal information of the original image sequence can be preserved completely by constructing the holistic STTM. Then, according to the spatial-temporal related prior knowledge of the target and background, the target detection task is customized as an optimization problem of low rank and sparse tensor recovery. To better preserve the internal structure and capture more global information, the tensor spectral k -support norm minimization is introduced as the regularization term of the constraint background. Finally, draw support from the framework of alternating direction method of multipliers (ADMM) algorithm, the precise separation of target and background is achieved. In addition, to promote the prosperity of sequential detection methods, we released to the scientific community a small IR target dataset containing six image sequences with urban background. The experimental results on six real IR sequences demonstrate that our method outputs the most outstanding detection performance compared with the latest sequential detection methods.

Fast and robust detection of infrared small targets in a single image has always been challenging. The background residue in complex ground–sky background images leads to high false alarm rates when traditional local contrast methods are used because of the complexity and variability of the ground–sky background imaging environment. A weighted local ratio-difference contrast (WLRDC) method is proposed in this paper to address this problem and detect infrared small targets in the ground–sky background. First, target candidate pixels are obtained using a simple facet kernel filter. Second, local contrast saliency maps and weighted mappings are calculated on the basis of the local ratio-difference contrast and the spatial dissimilarity of the target, respectively. Third, the final weighted mapping can be obtained through the multiplication fusion strategy. Finally, a simple threshold segmentation method is employed to extract the target. Experimental results on six real ground–sky infrared scenes showed that the proposed method outperforms existing state-of-the-art methods.

Spot positioning accuracy is an important index of laser processing system and ranging system. When the laser spot is noisy or the gray level is not uniform, the positioning accuracy is easily affected. Aiming at the above problems, this paper proposes a laser spot segmentation method based on the Chan-Vese model, which can improve the accuracy of spot center localization in combination with the gray centroid method. Firstly, the laser spot image is decomposed by two-dimensional wavelet, and the high-frequency component is suppressed by soft threshold function to eliminate the noise in the laser spot image. Secondly, the level set algorithm based on Chan-Vese model is used to segment the laser spot image with adaptive improvement of the initial coordinates of the evolution curve. Finally, the center coordinates are calculated inside the segmentation curve using the gray centroid method. Experimental results show that the method is more accurate and robust.