Feature reconstruction networks have achieved remarkable performance in few-shot fine-grained classification tasks. Nonetheless, traditional feature reconstruction networks rely on linear regression. This linearity may cause the loss of subtle discriminative cues, ultimately resulting in less precise reconstructed features. Moreover, in situations where the background predominantly occupies the image, the background reconstruction errors tend to overshadow foreground reconstruction errors, resulting in inaccurate reconstruction errors. In order to address the two key issues, a novel approach called the Foreground-Aware Kernelized Feature Reconstruction Network (FKFRN) is proposed. Specifically, to address the problem of imprecise reconstructed features, we introduce kernel methods into linear feature reconstruction, extending it to nonlinear feature reconstruction, thus enabling the reconstruction of richer, finer-grained discriminative features. To tackle the issue of inaccurate reconstruction errors, the foreground-aware reconstruction error is proposed. Specifically, the model assigns higher weights to features containing more foreground information and lower weights to those dominated by background content, which reduces the impact of background errors on the overall reconstruction. To estimate these weights accurately, we design two complementary strategies: an explicit probabilistic graphical model and an implicit neural network-based approach. Extensive experimental results on eight datasets validate the effectiveness of the proposed approach for few-shot fine-grained classification.

Fine-grained ship recognition endeavors to accurately locate ship targets and recognize their respective fine-grained categories. Current ship recognition methods primarily rely on the feature pyramid network (FPN) for extracting multiscale features. However, FPN exhibits a spatial misalignment issue when fusing features from adjacent-scale feature maps, leading to an inability to extract fine-grained features. Consequently, this limitation constrains the fine-grained recognition capabilities of these recognition methods. Moreover, ship targets possess a high level of intraclass diversity and interclass similarity, yet existing recognition models struggle to extract features with strong category separability, resulting in weakened fine-grained ship recognition performance. In order to solve the spatial misalignment problem that occurs in FPN, a spatial-aligned FPN (SAFPN) is investigated. SAFPN employs a spatial-aware alignment fusion module (SAFM) to effectively extract rich fine-grained features between adjacent-scale feature maps. Moreover, in response to the challenge posed by low category separability in features due to the intraclass diversity and interclass similarity among ship targets, an adaptive prototypical contrastive learning (APCL) method is further proposed. By introducing prototypical contrastive loss, APCL effectively enhances the category separability of ship features, thereby improving the performance of fine-grained ship recognition. Numerous experiments are validated on two fine-grained ship recognition datasets: FGSD and ShipRSImageNet. The experimental results demonstrate that the proposed SAFPN and APCL facilitate the model in extracting fine-grained features with strong category separability, effectively enhancing the performance of fine-grained ship recognition. Our code will be public and available athttps://github.com/liyangfan0/Fine-Grained-Ship-Recognition.

Fine-grained classification is of significant importance in the field of remote sensing. However, obtaining valuable and rare target images is often a challenging task, giving rise to the few-shot fine-grained classification problem. In response to this challenge, various meta-learning approaches have been introduced, with the feature map reconstruction network emerging as a prominent method. Targets in remote sensing images exhibit arbitrary orientation, substantial inter-class similarity and intra-class diversity. Nevertheless, the conventional feature map reconstruction network exhibits subpar performance due to its inability to handle rotational variations. Moreover, it only reconstructs features from a single channel dimension of support features, neglecting the interplay between different dimensions and resulting in inaccurate reconstruction errors. To overcome the challenges of imprecise rotational variation features for reconstruction and inaccurate reconstruction errors, we propose a rotation-invariant feature map complementary reconstruction network (RIFCRN). The RIFCRN involves several key innovations. First, we introduce a novel rotation-invariant module (RIM) based on active rotating filters and oriented response pooling, enabling the extraction of rotation-invariant features for reconstruction. This modification enhances the suitability of the feature map reconstruction network for the few-shot fine-grained classification problem. Second, we put forward a novel feature map complementary reconstruction (CPR) method that calculates the complementary reconstruction errors (CRE) which effectively captures relationships among different feature map dimensions and results in more accurate reconstruction errors. Finally, extensive experiments have been conducted to validate the effectiveness of the proposed RIFCRN in addressing the few-shot fine-grained classification problem. The code will be available at https://github.com/liyangfan0/RIFCRN.

With the advancement of high-resolution remote sensing satellites, the tracking of high-value targets such as planes and ships within satellite videos has become imperative. In recent years, several object tracking methods designed for satellite videos based on correlation filters have been proposed. However, these traditional correlation filters typically identify the location with the highest response value on the response map as the target position. In the context of satellite videos, where targets are often very small and surrounded by numerous similar objects, depending only on response values to determine the target’s location can easily lead to interference from nearby objects, resulting in tracking failures. Moreover, targets frequently encounter occlusion during their motion, further complicating tracking tasks due to the absence of distinctive target appearance features and leading to the issue of model drift. To address these challenges, we propose a novel distractor-occlusion aware correlation particle filters. Instead of determining the position with the maximum response value, our method initially selects the top k response values from the response map, creating a pool of candidates. Subsequently, we introduce an innovative quality score, rooted in motion information and response scores related to the target, for each candidate. Finally, these quality scores are employed to filter the most suitable candidate. This novel distractor-aware module effectively equips our tracking method to perform well in the presence of distractors. Additionally, to handle occlusion, we integrate the occlusion-aware module into the correlation particle filters, improving the tracker’s performance in occluded scenarios. To ensure the effective collaboration of the distractor-aware module and the occlusion-aware module, we introduce the dual Kalman Filter method. Our comprehensive experiments conducted on the SatSOT datasets conclusively demonstrate the effectiveness and superiority of our proposed tracking method. The code will be available at https://github.com/liyangfan0/DOCPF.

Feature pyramid network transformer decoder (FPNFormer) module, which can effectively deal with the strong rotation arbitrary of remote sensing images while improving the expressiveness and robustness of the model. It is a plug-and-play module that can be well transferred to various detection models and significantly improves performance. Specifically, we use the computational method of transformer decoder to deal with the problem that the image has any orientation, and its output weakly depends on the order of the input data. We apply it to the feature fusion stage and design two ways top-down and down-top to fuse features of different scales, which enables the model to have a more vital ability to perceive objects at different scales and angles. Experiments on commonly used benchmarks (DOTA1.0, DOTA1.5, SSDD, and RSDD) demonstrate that the proposed FPNFormer module significantly improves the performance of multiple arbitrary-oriented object detectors, such as 1.99% map improvement of rotated retinanet on DOTA’s cross-validation set. On RSDD datasets, the baseline model using FPNFormer improves the map of large objects by 5.1%. Combined with more competitive models, the proposed method can achieve a 79.39% map on the DOTA1.0 dataset. The code is available athttps://github.com/bityangtian/FPNFormer.

Fine-grained ship classification (FGSCR) has many applications in military and civilian fields. In recent years, deep learning has been widely used for classification tasks, and its success is inseparable from that of big data. However, ship images are valuable, with only a few images of a specific category being obtained, leading to the fine-grained few-shot ship classification problem. In addition, feature map channels contain distinct characteristics and discriminative details, which significantly influence FGSCR. Intuitively, channels with distinct characteristics should be assigned larger weights for classification, but most few-shot learning methods treat the channels equally. Therefore, we propose a generalized ridge-regression-based channelwise feature map weighted reconstruction network to address these issues. First, we reconstruct the query feature map by assigning different weights to the support feature map channels using the generalized ridge regression method. The channels with large discriminative details contribute more toward reconstruction. Second, we propose a support channel weight module to calculate the channel weight matrix used in the generalized ridge regression method. Finally, based on the reconstructed query feature map, we can calculate the reconstruction error. The reconstruction error is adopted as the distance metric. Our proposed method achieves excellent performance on the fine-grained ship, bird, aircraft, and WHU-RS19 datasets compared with other representative few-shot learning methods. Considering the limited studies on the fine-grained few-shot ship classification problem, we believe that our work is of great significance.

Target occlusion is common in satellite videos, which makes object tracking difficult because most state-of-the-art trackers are not robust to occlusion, particularly complete occlusion. In this letter, we propose a novel correlation filter algorithm with an interacting multiple model (IMM) for object tracking in satellite videos that combines the strength of the correlation filter and the IMM. When the target is occluded, we utilize the IMM to predict target position. Therefore, the proposed tracker is robust to occlusion. The experimental results demonstrate that our tracker performs favorably when the target is occluded and achieves excellent performance compared with state-of-the-art methods.

Fine-grained ship classification plays an important part in many military and civilian applications. However, it is often costly to obtain images of ships, making it difficult to procure large numbers of such images. This difficulty poses challenges to machine learning procedures that require ship images. Commonly, only a few input images are available for certain types of ships, which leads to the poor generalization of trained models. Therefore, few-shot fine-grained ship classification is an important (but significantly challenging) task in machine learning. In this study, we propose a novel foreground-aware feature map reconstruction network (FRN) that is simple, effective, and scalable. We reconstruct the query features from support features using ridge regression and predict the distribution of the categories of query images between the reconstructed and real query features by comparing the weighted distances with foreground weights. The foreground weights indicate the percentages of foreground information in the feature map locations. We propose two methods for calculating the foreground weights: a non-parametric method and a parametric method. Our proposed network achieves state-of-the-art results on both the fine-grained ship classification dataset Fine-Grained Ship Classification in Remote sensing images (FGSCR) and the natural fine-grained bird classification dataset Caltech UCSD Birds (CUB).

Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a correlation particle filter algorithm with motion estimation for object tracking in satellite videos. The tracker, called CPKF, combines the strengths of the correlation, particle, and Kalman filters. Compared with existing tracking methods based on correlation filters, the proposed tracker has three major advantages: (1) Particle sampling, and motion estimation build robustness against partial and complete occlusion. (2) Color histogram model makes it robust to target rotation. (3) Fusion of multiple feature response maps effectively handle background clutter and low contrast. The experimental results demonstrate that the proposed tracking algorithm performs better than state-of-the-art methods.