Efficient detection of targets immersed in a complex background with a low signal-to-clutter ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this paper, we address the target detection problem in terms of local image segmentation and propose a novel small target detection algorithm derived from facet kernel and random walker (RW) algorithm which includes four main stages. First, since the RW algorithm is suitable for images with less noises, local order-statistic and mean filtering are applied to remove the pixel-sized noises with high brightness (PNHB) and smooth the infrared images. Second, the infrared image is filtered by the facet kernel to enhance the target pixels and candidate target pixels are extracted by an adaptive threshold operation. Third, inspired by the properties of infrared targets, a novel local contrast descriptor (NLCD) based on the RW algorithm is proposed to achieve clutter suppression and target enhancement. Then, the candidate target pixels are selected as central pixels to construct the local regions and the NLCD map of all local regions is computed. The obtained NLCD map is weighted by the filtered map of facet kernel to further enhance target. Finally, the target is detected by a thresholding operation on the weighted map. Experimental results on three data sets show that the proposed method outperforms conventional baseline methods in terms of target detection accuracy.

Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images for improving the accuracy of CD is still a challenge. To address that, a novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) is proposed in this article. First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images. Then, to alleviate the semantic gap between low-level features and high-level features, we propose an adjacent-level feature cross-fusion (AFCF) module to aggregate complementary feature information between the adjacent levels. Furthermore, the full-scale skip connection strategy is introduced to improve the capability of pixel-wise prediction and the compactness of changed objects in the results. Finally, the proposed AFCF3D-Net has been validated on the three challenging remote sensing CD datasets: the Wuhan building dataset (WHU-CD), the LEVIR building dataset (LEVIR-CD), and the Sun Yat-Sen University dataset (SYSU-CD). The results of quantitative analysis and qualitative comparison demonstrate that the proposed AFCF3D-Net achieves better performance compared to other state-of-the-art methods. The code for this work is available at https://github.com/wm-Githuber/AFCF3D-Net.

Identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R ₂ FD ₂ ) that is robust to radiation and rotation differences, which consists of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor (MALG) is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor (RMLG), which includes fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to improve RMLG’s resistance to radiation and rotation variances. Finally, we conduct experiments to validate the matching performance of our R ₂ FD ₂ utilizing different types of multimodal image datasets. Experimental results show that the proposed R ₂ FD ₂ outperforms five state-of-the-art feature matching methods. Moreover, our R ₂ FD ₂ achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over contrastive methods.

Image matching is a key preprocessing step for the integrated application of synthetic aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences between such images, automatic matching for them is still quite challenging. Recently, structure features have been effectively applied to SAR-to-optical image matching because of their robustness to nonlinear intensity differences. However, structure features designed by handcraft are limited to achieve further improvement. Accordingly, this letter employs the deep learning technique to refine structure features for improving image matching. First, we extract multiorientated gradient features to depict the structure properties of images. Then, a shallow pseudo-Siamese network is built to convolve the gradient feature maps in a multiscale manner, which produces the multiscale convolutional gradient features (MCGFs). Finally, MCGF is used to achieve image matching by a fast template scheme. MCGF can capture finer common features between SAR and optical images than traditional handcrafted structure features. Moreover, it also can overcome some limitations of current matching methods based on deep learning, which requires solving a huge number of model parameters by a large number of training samples. Two sets of SAR and optical images with different resolutions are used to evaluate the matching performance of MCGF. The experimental results show its advantage over other state-of-the-art methods.

For remote sensing object detection, fusing the optimal feature information automatically and overcoming the sensitivity to adapt multi-scale objects remains a significant challenge for the existing convolutional neural networks. Given this, we develop a convolutional network model with an adaptive attention fusion mechanism (AAFM). The model is proposed based on the backbone network of EfficientDet. Firstly, according to the characteristics of object distribution in datasets, the stitcher is applied to make one image containing objects of various scales. Such a process can effectively balance the proportion of multi-scale objects and handle the scale-variable properties. In addition, inspired by channel attention, a spatial attention model is also introduced in the construction of the adaptive attention fusion mechanism. In this mechanism, the semantic information of the different feature maps is obtained via convolution and different pooling operations. Then, the parallel spatial and channel attention are fused in the optimal proportions by the fusion factors to get the further representative feature information. Finally, the Complete Intersection over Union (CIoU) loss is used to make the bounding box better cover the ground truth. The experimental results of the optical image dataset DIOR demonstrate that, compared with state-of-the-art detectors such as the Single Shot multibox Detector (SSD), You Only Look Once (YOLO) v4, and EfficientDet, the proposed module improves accuracy and has stronger robustness.

We study the problem of object detection in remote sensing images. As a simple but effective feature extractor, Feature Pyramid Network (FPN) has been widely used in several generic vision tasks. However, it still faces some challenges when used for remote sensing object detection, as the objects in remote sensing images usually exhibit variable shapes, orientations, and sizes. To this end, we propose a dedicated object detector based on the FPN architecture to achieve accurate object detection in remote sensing images. Specifically, considering the variable shapes and orientations of remote sensing objects, we first replace the original lateral connections of FPN with Deformable Convolution Lateral Connection Modules (DCLCMs), each of which includes a $3\times 3$ deformable convolution to generate feature maps with deformable receptive fields. Additionally, we further introduce several Attention-based Multi-Level Feature Fusion Modules (A-MLFFMs) to integrate the multi-level outputs of FPN adaptively, further enabling multi-scale object detection. Extensive experimental results on the DIOR dataset demonstrated the state-of-the-art performance achieved by the proposed method, with the highest mean Average Precision (mAP) of 73.6%.