Orientation-First Strategy With Angle Attention Module for Rotated Object Detection in Remote Sensing Images

Zhang, Yuxi; Wang, Yongcheng; Zhang, Ning; Li, Zheng; Zhao, Zhikang; Gao, Yunxiao; Xu, Dongdong; Ben, Guangli

doi:10.1109/jstars.2022.3209349

Cited by 3 publications

(2 citation statements)

References 71 publications

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“…T HE advancement of satellite technology has facilitated the easy acquisition of high-resolution RSI in recent years, thereby making RSI applications such as object detection [1], [2] and change detection [3], [4] a prominent focus in the field of computer vision. Among these applications, instance segmentation holds particular significance as it not only identifies and classifies objects in each image but also segments each object at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

DCTC: Fast and Accurate Contour-Based Instance Segmentation With DCT Encoding for High-Resolution Remote Sensing Images

Chen,

Liu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Instance segmentation in Remote Sensing Images (RSI) poses significant challenges due to the diverse scales of targets, scene complexity, and a high number of targets, making most methods struggle with suboptimal performance and time-consuming computations. To solve those problems, a fast and accurate RSI instance segmentation model (named DCTC) is designed in this paper. DCTC transforms classification problem into regression problem to improve the reference speed. DCTC contains two parallel branches. The contour branch performs iterative regression on contours, extracting precise contour information to improve boundary accuracy. Meanwhile, the DCT branch refines mask predictions and supplements instance context information, which particularly benefits the segmentation of small targets. DCT encoding is employed in the DCT branch to convert the mask representation into DCT format, aligning the outputs of the contour and DCT branches. Three innovative modules are introduced in the DCT branch: the Coarse Result Generation module (CRG), Iteratively Deform and Regress module (IDR), and Contour and DCT Fusion module (CDF). The CRG module generates coarse DCT vectors and contour coordinates, facilitating information exchange between the contour and DCT branches. The IDR module iteratively refines DCT vectors, enabling DCTC to focus more on small targets and instance details. The CDF module merges DCT vectors and contour coordinates, ensuring effective interaction between boundary and context information, thereby enhancing performance. Extensive experiments demonstrate the superiority of DCTC, which achieves 67.7, 36.3, 67.4, 55.1AP on NWPU VHR-10, iSAID, SAR Ship Detection Dataset (SSDD), and High-Resolution SAR Images Dataset (HRSID), and ranks first among state-of-the-art methods while maintaining real-time processing capability. Furthermore, DCTC exhibits strong performance on both optical and SAR images, and the designed DCT branch can be simply plug into any contour-based method to improve the network performance.

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Section: Introductionmentioning

confidence: 99%

DCTC: Fast and Accurate Contour-Based Instance Segmentation With DCT Encoding for High-Resolution Remote Sensing Images

Chen,

Liu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, with the development of graphics processing unit (GPU) computing performance, deep learning has emerged and been applied to various fields. It has changed the processing methods of remote sensing data, such as object detection [18,19], superresolution [20], and change detection [21]. Deep learning-based HSI classification has become a research hot spot in this field.…”

Section: Introductionmentioning

confidence: 99%

Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey

Feng,

Wang,

et al. 2023

Remote Sensing

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In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed.

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Feature Enhancement and Alignment for Oriented Object Detection

Xie,

You,

Chen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Over the last few years, developments in fields such as aviation and remote sensing have drawn increasing attention to the detection of rotated objects. Unlike general object detection, rotated object detection requires overcoming certain challenges, such as detecting objects with different directions and high aspect ratios. Recently proposed rotated object detectors have achieved good results, but most of them rely on hand-designed anchors, which require manual adjustment of the anchors settings in different scenarios. On the contrary, this paper presents a method called FEADet, an anchor-free detector that utilizes feature enhancement and alignment to achieve competitive performance, without the use of anchors. Specifically, in order to better fuse features across different layers, we design an attention feature fusion (AAF) module to reduce the aliasing effect produced by the fusion of different layers. To deal with feature misalignment in detecting objects with orientation, we propose an adaptive alignconv (AAC) module, which is implemented by the constrained deformable convolution and align convolution. The ACC module can efficiently extract object features according to the decoded boxes and predicted constrained offsets. On the two benchmark datasets DOTA and HRSC2016, a comprehensive evaluation of our method has been conducted to demonstrate the effectiveness of these method in comparison with state-of-the-art methods.

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Orientation-First Strategy With Angle Attention Module for Rotated Object Detection in Remote Sensing Images

Cited by 3 publications

References 71 publications

DCTC: Fast and Accurate Contour-Based Instance Segmentation With DCT Encoding for High-Resolution Remote Sensing Images

DCTC: Fast and Accurate Contour-Based Instance Segmentation With DCT Encoding for High-Resolution Remote Sensing Images

Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey

Feature Enhancement and Alignment for Oriented Object Detection

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