Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via using simpler object representation model and less regression parameters. In P-RSDet method, arbitrary-oriented object detection can be achieved by predicting the center point and regressing one polar radius and two polar angles. Besides, in order to express the geometric constraint relationship between the polar radius and the polar angle, a Polar Ring Area Loss function is proposed to improve the prediction accuracy of the corner position. Experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show that our P-RSDet achieves state-of-the-art performances with simpler model and less regression parameters.

Motivated by the development of deep convolution neural networks (DCNNs), aircraft detection has gained tremendous progress. State-of-the-art DCNN-based detectors mainly belong to top-down approaches, which enumerate massive potential locations of aircraft with the form of rectangular regions, and then identify whether they are objects or not. Compared with these top-down detectors, this article shows that aircraft detection via a type of bottom-up method can have better performances in the era of deep learning. In this article, we propose a novel bottom-up detector named X-LineNet. It formulates the aircraft detection task as prediction and clustering of paired intersecting line segments inside each target. Aircraft detection is then a purely appearance-based line segments estimation problem, without any rectangular regions classification or implicit features learning. With simple postprocessing, X-LineNet can simultaneously provide multiple representation forms of the detection result: the horizontal bounding box, the oriented bounding box, and the pentagonal mask. The pentagonal mask is a more accurate representation form of aircraft which has less redundancy than that of a rectangular box. Experiments show that X-LineNet outperforms prevalent top-down and region-based detectors on UCAS-AOD, NWPU VHR-10, and DIOR public data sets in the field of aircraft detection.

Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and so forth. Even advanced large vision-language models (LVLMs) with billions of parameters struggle to handle such tasks satisfactorily. To address this, we propose OneChart: a reliable agent specifically devised for the structural extraction of chart information. Similar to popular LVLMs, OneChart incorporates an autoregressive main body. Uniquely, to enhance the reliability of the numerical parts of the output, we introduce an auxiliary token placed at the beginning of the total tokens along with an additional decoder. The numerically optimized (auxiliary) token allows subsequent tokens for chart parsing to capture enhanced numerical features through causal attention. Furthermore, with the aid of the auxiliary token, we have devised a self-evaluation mechanism that enables the model to gauge the reliability of its chart parsing results by providing confidence scores for the generated content. Compared to current state-of-the-art (SOTA) chart parsing models, e.g., DePlot, ChartVLM, ChartAst, OneChart significantly outperforms in Average Precision (AP) for chart structural extraction across multiple public benchmarks, despite enjoying only 0.2 billion parameters. Moreover, as a chart parsing agent, it also brings 10%+ accuracy gains for the popular LVLM (LLaVA-1.6) in the downstream ChartQA benchmark. Our code, model and datasets ara available at https://onechartt.github.io.

Object detectors of remote sensing (RS) imagery with deep learning have become increasingly popular and rely heavily on extensive labeled data. The source-only detectors, which are trained on massive labeled data in a source domain, in some cases fail to get satisfactory performance on a target domain due to the domain shift. To alleviate the domain shift, popular approaches consider feature distribution alignment, but the target domain with massive unlabeled data is under-utilized. Some methods use the source-only model to generate pseudo labels for target domain data, but the variation of different remote sensing scenarios produces the domain shift, which is injected directly into the pseudo labels. Therefore, We propose the Dual-head rectification Domain Adaptation network (DualDA-Net) to alleviate the domain shift and exploit the potential of unlabeled target domain data. DualDA-Net cooperates the coarse-to-fine consistency alignment (CCA) with dual-head co-training (DHCT) to align the distribution and generate pseudo labels progressively. Specifically, the CCA focuses on source and target domain feature distribution alignment via coarse-to-fine consistency alignment on multi-level features. Moreover, the DHCT with dual detection heads is deployed in the teacher-student framework, where one of the heads complements the other with high-quality predictions to rectify the pseudo labels as supervision and alleviate the biased information. Sufficient experiments have been conducted on several domain adaptation settings. The experimental results demonstrate that our DualDA-Net achieves success in the target domain for cross domain object detection of RS imagery.

Abstract—Remote sensing images semantic segmentation is a fundamental yet challenging task, which has long relied heavily on sufficient pixel-wise annotations. Semi-supervised learning is proposed to address the problem of high dependence on labeled data by exploiting more learnable samples generated from the large amounts of accessible unlabeled data. However, affected by the complexity and diversity of remote sensing images, various misclassifications often occur and lead to errors accumulation during model training. Errors accumulation will destroy the consistency of model training and lead to degradation of final segmentation performance. In this paper, in order to further alleviate the damage caused by the errors to the consistency of model training and improve final segmentation accuracy, we propose a novel semi-supervised segmentation framework, paradigms integration and contrastive selection (PICS). Firstly, multiple proven semi-supervised paradigms are integrated to generate pseudo-labeled samples with less noise. Secondly, a loss-based contrastive selection method is explored to distinguish generated samples that contain different degrees of inevitable misclassification, thereby further maintaining the approximation of the generated samples and the ground truth in the sample space. By generating and selecting high-quality pseudo-labeled samples for selective self-training, we can better guarantee the consistency during model training and obtain better segmentation results. Extensive experiments over the ISPRS Vaihingen, Potsdam and the challenging iSAID benchmarks demonstrate that our method yields significant accuracy boosting on the segmentation results and achieves on-par performance with the state-of-the-arts.