Deep learning algorithms have recently provided new ideas for various change detection (CD) tasks, which have yielded promising results. However, accurately identifying urban land cover and land use (LCLU) changes remains challenging in the very high-resolution (HR) remote sensing images due to the difficulties in effectively modeling the features from ground objects with different times and different spatial locations. In this letter, a multiscale swin transformer (MST)-based deeply supervised network (MSTDSNet) is proposed for monitoring urban LCLU changes using bi-temporal very HR remote sensing images. The wider and deeper layer aggregation (WDLA) is first introduced to improve the distinguishability of multiscale features. Subsequently, MST is adopted to make the most of the available spatial information in the refined multiscale features. Moreover, channel-wise dependencies (CWDS) are integrated as a soft constraint to directly supervise WDLA. Experiments are conducted on the widely used SYSU-CD and LEVIR-CD datasets. Compared with other state-of-the-art CD methods, MSTDSNet provides favorable performance, with the highest F1 of 80.33% and 88.10%, respectively.

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Single-Frame Infrared Small Target Detection (SF-IRSTD) has grown in popularity due to its broad application. Several models based on Low-Rank and Sparse Decomposition (LRSD) have been proposed recently and have shown excellent performance. Nevertheless, these methods regard the non-low-rank sparse points as the targets, obscuring the distinction between the non-low-rank noise and the target in the infrared image. To address this issue, we consider that the targets usually have a high local salience compared to the noise and propose a novel method using High Local Variance, Low-Rank and Sparse Decomposition (HiLV-LRSD), identifying the sparse points with high local salience and non-low-rank as the targets and the remaining regions as the background. Specifically, we first use the local variance to represent local salience and propose an LV* norm to constrain the background’s low-rank and local variance. Then, we define an adaptively re-weighted L1 ( L _lv,1 ) norm to constrain the sparsity of the target and enhance the influence of local variance. Finally, we propose an optimization framework and solve it by a Partially Iterative Alternating Direction Method of Multipliers (PI-ADMM). We evaluate our proposed method on the publicly available dataset SIRST and compare it to 10 state-of-the-art SF-IRSTD methods. The results show that our proposed method outperforms these methods.

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems.