Object detection in remote sensing is a challenging task due to the arbitrary orientations of objects and the vast variation in the number of objects within a single image. For instance, one image may contain hundreds of small vehicles, while another may only have a single football field. Recently, DEtection TRansformer (DETR) and its variants have achieved great success in object detection by setting a fixed number of object queries and using bipartite graph matching for one-to-one label assignment. However, we have observed that bipartite graph matching can result in relative redundancy of object queries when the number of objects changes dramatically in an image. This relative redundancy can cause two problems: slower convergence during training and redundant bounding boxes during inference. To analyze the aforementioned problems, we proposed a metric, Redundancy of Object Query (ROQ), to quantitatively analyze the redundancy. Through experiments, we discovered that the reason for the two issues is the difficulty in distinguishing between high-quality negative samples and positive samples. In this paper, we proposed Efficient-Matching Oriented Object Detection with Transformers(EMO2-DETR) consisting of three dedicated components to address the aforementioned issues. Specifically, Reassign Bipartite Graph Matching (RBGM) is proposed to extract high-quality negative samples from the negative samples. And Ignored Sample Predicted Head (ISPH) is proposed to predict high-quality negative samples. Then, Reassigned Hungarian loss is used to better involve high-quality negative samples in the update of model parameters. Extensive experiments on DOTAv1 and DOTAv1.5 datasets demonstrated that our proposed method achieves competitive results.

Visual Grounding for Remote Sensing(RSVG) aims to detect objects in remote sensing scenes based on textual descriptions. While existing methods perform well on RSVG datasets, they are limited to single-object predictions, making them unsuitable for multi-object candidate category datasets. Open-set methods can be applied to both RSVG and candidate datasets, but their use in remote sensing remains rare. To bridge this gap, we introduce the open-set approach to RSVG and propose Efficient Grounding DINO, using Grounding DINO as a baseline. Open-set methods rely on two key modules: cross-modality fusion and label assignment. Existing cross-modality fusion methods simultaneously update text and multi-scale visual features, which hampers model's ability to generalize under different texts and increases learning complexity. Existing methods predict a single object, allowing direct use as a positive example for loss calculation, while open-set methods for multi-objects require one-to-one matching to assign positive and negative samples. However, background interference in the RSVG datasets causes frequent misassignments, slowing model convergence. We address these issues with two innovations: the Multi-Scale Image-to-Text Fusion Module (MSITFM), which updates text features using self-attention to maintain independence from visual features and employs scale-specific cross-attention for multi-scale visual feature fusion to reduce learning complexity, achieving a 3% parameter and 21.6% GFLOPs reduction. Text Confidence Matching incorporates IoU-based confidence into label assignment to reduce mismatches and enhance model performance. Experiments on DIOR-RSVG, RSVG-HR, and DOTA datasets validate the effectiveness of our approach.

Satellite-based PM_2.5 estimation is an effective means to achieve large-scale and long-term PM_2.5 monitoring and investigation. Currently, most of methods retrieve PM_2.5 from satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PM_2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PM_2.5 concentrations from satellite infrared data, thus achieve the PM_2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PM_2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PM_2.5 concentrations correlates with ground-based data well with R² of 0.79 and RMSE of 15.43 μg · m⁻³ for hourly PM_2.5 estimation. Spatiotemporal distributions of model-estimated PM_2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PM_2.5 concentrations during daytime and nighttime in real-time monitoring.

Target detection in hyperspectral images (HSIs) aims to distinguish target pixels from the background using knowledge gleaned from prior spectra. Most traditional methods are based on certain assumptions and utilize handcrafted classifiers. These simple models and assumptions’ failure restrict the detection performance under complicated background interference. Recently, based on the convolutional networks, many supervised deep learning detectors have outperformed the traditional methods. However, these methods suffer from unstable detection, heavy computation burden, and optimization difficulty. This paper proposes a Siamese fully connected based target detector (SFCTD) that comprises nonlinear feature extraction modules (NFEMs) and cosine distance classifiers. Two NFEMs, which extract discriminative spectral features of input spectra-pairs, are based on fully connected layers for efficient computing and share the parameters to ease the optimization. To solve the few samples problem, we propose a pseudo data generation method based on the linear mixed model and the assumption that background pixels are dominant in HSIs. For mitigating the impact of stochastic suboptimal initialization, we parallelly optimize several Siamese detectors with small computation burdens and aggregate them as ensembles in the inference time. The network ensembles outperform every detector in terms of stability and achieve an outstanding balance between background suppression and detection rate. Experiments on multiple data sets demonstrate that the proposed detector is superior to the state-of-the-art detectors.

Modern object detection for aerial images requires numerous annotated data. However, the data annotation process inevitably introduces noise due to the bird’s eye view perspective of aerial images and the professional requirements of annotations. While recent noise-robust object detection methods achieved great success, the noise side-effect during the early training stage was still a problem. As demonstrated in this letter, noise during the early training stage will cumulatively affect the final performance. Based on the above observations, we propose a training strategy called correction maximization training to purify the noisy annotations and then train models. Specifically, we design a novel noise filter called the probability differential (PD) to identify and revise wrong labels. After purification, we train the detector with the revised dataset. Compared to existing works, the proposed method could be adapted in most modern object detectors (e.g., Faster RCNN and RetinaNet) and requires little hyperparameter tuning across different datasets and models. Extensive experiments on DOTA show that the proposed method achieves state-of-the-art results with both symmetric and asymmetric noise.

Remote sensing image scene classification (RSISC) plays a vital role in remote sensing applications. Recent methods based on convolutional neural networks (CNNs) have driven the development of RSISC. However, these approaches are not adequate considering the contributions of different features to the global decision. In this paper, triplet-metric-guided multi-scale attention (TMGMA) is proposed to enhance task-related salient features and suppress task-unrelated salient and redundant features. Firstly, we design the multi-scale attention module (MAM) guided by multi-scale feature maps to adaptively emphasize salient features and simultaneously fuse multi-scale and contextual information. Secondly, to capture task-related salient features, we use the triplet metric (TM) to optimize the learning of MAM under the constraint that the distance of the negative pair is supposed to be larger than the distance of the positive pair. Notably, the MAM and TM collaboration can enforce learning a more discriminative model. As such, our TMGMA can avoid the classification confusion caused by only using the attention mechanism and the excessive correction of features caused by only using the metric learning. Extensive experiments demonstrate that our TMGMA outperforms the ResNet50 baseline by 0.47% on the UC Merced, 1.46% on the AID, and 1.55% on the NWPU-RESISC45 dataset, respectively, and achieves performance that is competitive with other state-of-the-art methods.