With the abomination of time-consuming or even impractical large-scale labeling, few-shot object detection (FSOD) based on natural scenes has attracted extensive attention. However, directly migrating FSOD methods designed for natural images to large-size remote sensing images (RSIs) still remains challenges. 1) Labels of novel instances within the base dataset are inconsistently assigned between the base training and the few-shot fine-tuning stage, which confuses the detector and leads to significant performance degradation over novel classes. 2) The region proposal network (RPN) of detectors cannot provide sufficient high-quality proposals for remote sensing objects with various aspect ratios and irregular shapes, leading to decreased detection performance. To tackle these issues, we specify a novel few-shot object detector for RSIs, to avoid the significant performance degradation caused by inconsistent labeling assignments, as well as efficiently leveraging the novel instances that existed in the base dataset. Furthermore, the proposed detector utilizes a coarse-to-fine regression method with an enhanced feature extractor called Gradual RPN to improve the recall of RPN. Experiments on a newly constructed few-shot detection benchmark show that our approach improves the mAP of novel classes by up to 8.4% and the average recall of RPN by up to 12.3%. The source code is available at here.

Object detection, particularly open-vocabulary object detection, plays a crucial role in Earth sciences, such as environmental monitoring, natural disaster assessment, and land-use planning. However, existing open-vocabulary detectors, primarily trained on natural-world images, struggle to generalize to remote sensing images due to a significant data domain gap. Thus, this paper aims to advance the development of open-vocabulary object detection in remote sensing community. To achieve this, we first reformulate the task as Locate Anything on Earth (LAE) with the goal of detecting any novel concepts on Earth. We then developed the LAE-Label Engine which collects, auto-annotates, and unifies up to 10 remote sensing datasets creating the LAE-1M — the first large-scale remote sensing object detection dataset with broad category coverage. Using the LAE-1M, we further propose and train the novel LAE-DINO Model, the first open-vocabulary foundation object detector for the LAE task, featuring Dynamic Vocabulary Construction (DVC) and Visual-Guided Text Prompt Learning (VisGT) modules. DVC dynamically constructs vocabulary for each training batch, while VisGT maps visual features to semantic space, enhancing text features. We comprehensively conduct experiments on established remote sensing benchmark DIOR, DOTAv2.0, as well as our newly introduced 80-class LAE-80C benchmark. Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method.

Few-shot object detection has attracted extensive attention due to the abomination of time-consuming or even impractical large-scale data labeling. Current studies attempted to employ prototype-matching approaches for object detection, constructing class prototypes from textual or visual features. However, single visual prototypes exhibit limited generalization in few-shot scenarios, while single textual prototypes lack the spatial details of remote sensing targets. Therefore, to achieve the best of both worlds, we propose a prototype aggregating module to integrate textual and visual prototypes, leveraging both semantics from textual prototypes and spatial details from visual prototypes. In addition, the transferability of multi-modal few-shot detectors from natural scenarios to remote sensing scenarios remains unexplored, and previous training strategies for FSOD do not adequately consider the characteristics of text encoders. To address the issue, we have conducted extensive ablation studies on different feature extractors of the detector and propose an efficient two-stage training strategy, which takes the characteristics of the text feature extractor into account. Experiments on two common few-shot detection benchmarks demonstrate the effectiveness of our proposed method. In four widely used data splits of DIOR, our method significantly outperforms previous state-of-the-art methods by at most 8.7%.

Deep learning (DL)-based methods have shown great potential in the satellite video target tracking community. Nevertheless, most of the methods are still plagued by poor localization capabilities and background interference problems. In this paper, we propose EHTracker, a Siamese-based tracker based on enhanced head network, specifically for tiny targets in satellite videos. EHTracker enhances the localization capability for tiny objects through the proposed enhanced head network, while the proposed background suppression module (BSM) improves the network's ability to suppress background interference. Specifically, to alleviate the problem of overlapping between the foreground and background in the classification branch, we propose a multi-head graph attention refinement module (MGARM). Second, to enable the regression branch to determine the location of feature-poor small targets more accurately, we introduce a gradual regression strategy (GRS). MGARM and GRS together constitute the enhanced head network component of EHTracker. Further, in order to work with the enhanced head network to achieve classification and localization of small targets that lack distinctive features, we design a background suppression module. Extensive experiments are implemented on the SatSOT dataset and SV248S dataset, and the experimental results show that our method achieves state-of-the-art tracking performance.

Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is particularly prominent in numerous remote sensing applications such as endangered species monitoring and disaster assessment. Existing FSOD methods for remote sensing images (RSIs) have achieved promising progress but remain constrained by the limited diversity of instances. To address this issue, we propose a novel framework that can leverage a diffusion model pretrained on large-scale natural images to synthesize diverse remote sensing instances, thereby improving the performance of few-shot object detectors. Instead of directly synthesizing complete remote sensing images, we first generate instance-level slices via a specialized slice-to-slice module, and then embed these slices into full-scale imagery for enhanced data augmentation. To further adapt diffusion models for remote sensing scenarios, we develop a class-agnostic image inversion module that can invert remote sensing instance slices into semantic space. Additionally, we introduce contrastive loss to semantically align the synthesized images with their corresponding classes. Experimental results show that our method has achieved an average performance improvement of 4.4% across multiple datasets and various approaches. Ablation experiments indicate that the elaborately designed inversion module can effectively enhance the performance of FSOD methods, and the semantic contrastive loss can further boost the performance.

In data-scarcity scenarios, few-shot object detection (FSOD) methods exhibit a notable advantage in alleviating the over-fitting problem. Currently, research on FSOD in the field of remote sensing is advancing rapidly and FSOD methods based on the fine-tuning paradigm have initially displayed their excellent performance. However, existing fine-tuning methods often encounter classification confusion issues. This is potentially because of the shortage of explicit modeling for transferable common knowledge and the biased class distribution, especially for fine-grained targets with higher inter-class similarity and intra-class variance. In view of this, we first propose a decoupled self-distillation (DSD) method to construct class prototypes in two decoupled feature spaces and measure inter-class correlations as soft labels or aggregation weights. To ensure a robust set of class prototypes during the self-distillation process, we devise a feature filtering module (FFM) to preselect high-quality class representative features. Furthermore, we introduce a progressive prototype calibration module (PPCM) with two steps, compensating the base prototypes with the prior base distribution and then calibrating the novel prototypes with adjacent calibrated base prototypes. Experiments on MAR20 and customized SHIP20 datasets have demonstrated the superior performance of our method compared to other existing advanced FSOD methods, simultaneously confirming the effectiveness of all proposed components.