Detection In Remote Sensing Images Research Articles

Tiny Ship Detection in Remote Sensing Images

Tiny Ship Detection in Remote Sensing Images

Multi-Scale Object Detection in Remote Sensing Images Based on Feature Interaction and Gaussian Distribution

Remote sensing images are usually obtained from high-altitude observation. The spatial resolution of the images varies greatly and there are scale differences both between and within object classes, resulting in a diversified distribution of object scales. In order to solve these problems, we propose a novel object detection algorithm that maintains adaptability to multi-scale object detection based on feature interaction and Gaussian distribution in remote sensing images. The proposed multi-scale feature interaction model constructs feature interaction modules in the feature layer and spatial domain and combines them to fully utilize the spatial and semantic information of multi-level features. The proposed regression loss algorithm based on Gaussian distribution takes the normalized generalized Jensen–Shannon divergence with Gaussian angle loss as the regression loss function to ensure the scale invariance of the model. The experimental results demonstrate that our method achieves 77.29% mAP on the DOTA-v1.0 dataset and 97.95% mAP on the HRSC2016 dataset, which are, respectively, 1.12% and 1.41% higher than that of the baseline. These experimental results indicate the effectiveness of our method for object detection in remote sensing images.

Improving remote sensing object detection by using feature extraction and rotational equivariant attention

ABSTRACT In recent years, computer vision has witnessed significant attention in the research on object detection in remote-sensing images. Unlike objects in traditional natural images, those in remote sensing images are captured vertically by spacecraft, introducing arbitrary directionality, substantial scale variation, and a more complex background. We propose a rotation-equivariant detector enhanced with feature fusion and attention modules to address remote sensing image object detection challenges. Specifically, we introduce a Rotation-Enhanced Feature Extraction (REFE) module and a Rotational Equivariant Attention (REA) module. These enhancements empower the detector to extract object information more effectively from remotely sensed images, filtering out complex background information and improving detection accuracy and stability. Through extensive experiments on diverse and challenging remote sensing image datasets, our method outperforms the task of object detection. Remarkably, our network achieves 1.0, 2.91, and 1.11 mean average precision (mAP) improvements on the DOTA-v1.5, HRSC2016 and DIOR-R datasets. These experimental results robustly demonstrate the effectiveness and superiority of our proposed method.

Shadow Detection in Remote Sensing Images Based on Spectral Radiance Separability Enhancement.

Shadow detection is a basic task of remote sensing image analysis, but it is often seriously disturbed by vegetation, water bodies, and black objects. It is observed that vegetation and dark objects often show a dark look in visible bands but brighter in the near-infrared (NIR), and is also noticed that the reflection of inland water bodies in the green band is stronger than that in the blue band. Taking advantage of these physical properties and combining them with the bluish and dark appearance of shadows, we propose a simple but effective shadow detection method for multispectral remote sensing images. These physical properties are used to create transformation models that suppress features such as vegetation, water bodies, etc., but at the same time enhance shadows. Then, we transform the shadow representation into a color space to generate candidate shadows using dominant color components. To separate shadows from the others, we propose two indexes, the normalized Color Difference Composite Index (CDCI) and Color Purity Index (CPI), and fuse them to achieve shadows and their confidence. The experimental results indicate that the proposed method can effectively detect the shadows in multispectral images and outperforms the state-of-the-art approaches.

SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect high-value targets by solely utilizing image-level category labels; however, two problems have not been well addressed by existing methods. Firstly, the seed instances (SIs) are mined solely relying on the category score (CS) of each proposal, which is inclined to concentrate on the most salient parts of the object; furthermore, they are unreliable because the robustness of the CS is not sufficient due to the fact that the inter-category similarity and intra-category diversity are more serious in RSIs. Secondly, the localization accuracy is limited by the proposals generated by the selective search or edge box algorithm. To address the first problem, a segment anything model (SAM)-induced seed instance-mining (SSIM) module is proposed, which mines the SIs according to the object quality score, which indicates the comprehensive characteristic of the category and the completeness of the object. To handle the second problem, a SAM-based pseudo-ground truth-mining (SPGTM) module is proposed to mine the pseudo-ground truth (PGT) instances, for which the localization is more accurate than traditional proposals by fully making use of the advantages of SAM, and the object-detection heads are trained by the PGT instances in a fully supervised manner. The ablation studies show the effectiveness of the SSIM and SPGTM modules. Comprehensive comparisons with 15 WSOD methods demonstrate the superiority of our method on two RSI datasets.

Transfer Learning for Object Detection in Remote Sensing Images with YOLO

This investigation explores exchange learning methodologies for protest locations in inaccessible detecting pictures utilizing the YOLO design. Four unmistakable exchange learning calculations, specifically YOLO Fine-Tuning, Highlight Extraction, Space Versatile Preparing, and Knowledge Distillation are investigated and assessed on an assorted dataset. The tests illustrate noteworthy advancements in location execution, with YOLO Fine-Tuning accomplishing an exactness of 0.85, review of 0.78, F1 score of 0.81, and mean average precision (mAP) of 0.75. Highlight Extraction grandstands competitive comes about, with an accuracy of 0.87, a review of 0.80, an F1 score of 0.83, and a mAP of 0.78. Domain Adaptive Training exhibits predominant execution, accomplishing an exactness of 0.89, review of 0.82, F1 score of 0.85, and mAP of 0.80. Information Refining yields promising results, with a precision of 0.88, review of 0.81, F1 score of 0.84, and mAP of 0.79. These discoveries highlight the viability of exchange learning algorithms in upgrading the adaptability and precision of YOLO for protest locations in diverse inaccessible detecting scenarios. The study contributes important bits of knowledge to the field of further detecting, emphasizing the viable appropriateness of tailored exchange learning techniques for real-world applications.

Task-Aligned Oriented Object Detection in Remote Sensing Images

Oriented object detection (OOD) can recognize and locate various objects more precisely than horizontal object detection; however, two problems have not been satisfactorily resolved so far. Firstly, the absence of interactions between the classification and regression branches leads to inconsistent performance in the two tasks of object detection. Secondly, the traditional convolution operation cannot precisely extract the features of objects in extremely aspect ratio in remote sensing images (RSIs). To address the first problem, the task-aligned detection module (TADM) and the task-aligned loss function (TL) are proposed in this paper. On the one hand, a spatial probability map and a spatial offset map are inferred from the shared features in the TADM and separately incorporated into the classification and regression branches to obtain consistency in the two tasks. On the other hand, the TL combines employing the generalized intersection over union (GIoU) metric with classification loss to further enhance the consistency in the two tasks. To address the second problem, a two-stage detection framework based on alignment convolution (TDA) is proposed. The features extracted from the backbone network are refined through alignment convolution in the first stage, and the final OOD results are inferred from refined features in the second stage. The ablation study verifies the effectiveness of the TADM, TL, and TDA. The comparisons with other advanced methods, on two RSI benchmarks, demonstrate the overall effectiveness of our method.

Transformer-Based Multi-layer Feature Aggregation and Rotated Anchor Matching for Oriented Object Detection in Remote Sensing Images

Transformer-Based Multi-layer Feature Aggregation and Rotated Anchor Matching for Oriented Object Detection in Remote Sensing Images

Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images

Addressing the limitations, including low automation, slow recognition speed, and limited universality, of current mudslide disaster detection techniques in remote sensing imagery, this study employs deep learning methods for enhanced mudslide disaster detection. This study evaluated six object detection models: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX, conducting experiments on remote sensing image data in the study area. Utilizing transfer learning, mudslide remote sensing images were fed into these six models under identical experimental conditions for training. The experimental results demonstrate that YOLOX-Nano’s comprehensive performance surpasses that of the other models. Consequently, this study introduces an enhanced model based on YOLOX-Nano (RS-YOLOX-Nano), aimed at further improving the model’s generalization capabilities and detection performance in remote sensing imagery. The enhanced model achieves a mean average precision (mAP) value of 86.04%, a 3.53% increase over the original model, and boasts a precision rate of 89.61%. Compared to the conventional YOLOX-Nano algorithm, the enhanced model demonstrates superior efficacy in detecting mudflow targets within remote sensing imagery.

Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method

Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% ( 0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks.

Siamese Transformer-Based Building Change Detection in Remote Sensing Images.

To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change. The difference features before and after the change are then fused, and the fused difference features are used to generate a change map, which reduces the false-positive problem to a certain extent. Experiments were conducted on two publicly available building change detection datasets, LEVIR-CD and WHU-CD. The F1 scores for LEVIR-CD and WHU-CD reached 89.58% and 84.51%, respectively. The experimental results demonstrate that when utilized for building change detection in remote sensing images, the proposed method exhibits improved robustness and detection performance. Additionally, this method serves as a valuable technical reference for the identification of building damage in remote sensing images.

Co-ECL: Covariant Network with Equivariant Contrastive Learning for Oriented Object Detection in Remote Sensing Images

Co-ECL: Covariant Network with Equivariant Contrastive Learning for Oriented Object Detection in Remote Sensing Images

Small Object Detection in Remote Sensing Images Based on Redundant Feature Removal and Progressive Regression

Small Object Detection in Remote Sensing Images Based on Redundant Feature Removal and Progressive Regression

An intelligent water drop algorithm with deep learning driven vehicle detection and classification

<abstract> <p>Vehicle detection in Remote Sensing Images (RSI) is a specific application of object recognition like satellite or aerial imagery. This application is highly beneficial in different fields like defense, traffic monitoring, and urban planning. However, complex particulars about the vehicles and the surrounding background, delivered by the RSIs, need sophisticated investigation techniques depending on large data models. This is crucial though the amount of reliable and labelled training datasets is still a constraint. The challenges involved in vehicle detection from the RSIs include variations in vehicle orientations, appearances, and sizes due to dissimilar imaging conditions, weather, and terrain. Both specific architecture and hyperparameters of the Deep Learning (DL) algorithm must be tailored to the features of RS data and the nature of vehicle detection tasks. Therefore, the current study proposes the Intelligent Water Drop Algorithm with Deep Learning-Driven Vehicle Detection and Classification (IWDADL-VDC) methodology to be applied upon the Remote Sensing Images. The IWDADL-VDC technique exploits a hyperparameter-tuned DL model for both recognition and classification of the vehicles. In order to accomplish this, the IWDADL-VDC technique follows two major stages, namely vehicle detection and classification. For vehicle detection process, the IWDADL-VDC method uses the improved YOLO-v7 model. After the vehicles are detected, the next stage of classification is performed with the help of Deep Long Short-Term Memory (DLSTM) approach. In order to enhance the classification outcomes of the DLSTM model, the IWDA-based hyperparameter tuning process has been employed in this study. The experimental validation of the model was conducted using a benchmark dataset and the results attained by the IWDADL-VDC technique were promising over other recent approaches.</p> </abstract>

Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement

Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement

GAN-Based Sub-Instance Augmentation for Open-Pit Mine Change Detection in Remote Sensing Images

GAN-Based Sub-Instance Augmentation for Open-Pit Mine Change Detection in Remote Sensing Images

Oriented Object Detection in Remote Sensing Images Based on Feature Recombination

Oriented Object Detection in Remote Sensing Images Based on Feature Recombination

An Effective and Lightweight Hybrid Network for Object Detection in Remote Sensing Images

An Effective and Lightweight Hybrid Network for Object Detection in Remote Sensing Images

View-Consistency Network for Weakly Supervised Oriented Object Detection in Remote Sensing Images

View-Consistency Network for Weakly Supervised Oriented Object Detection in Remote Sensing Images

FADL-Net: Frequency-Assisted Dynamic Learning Network for Oriented Object Detection in Remote Sensing Images

FADL-Net: Frequency-Assisted Dynamic Learning Network for Oriented Object Detection in Remote Sensing Images