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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing"

共 4030 条记录，以下是11-20 订阅

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Discriminative Representation-Based Classifier for Few-Shot remote sensing Classification 7th

Discriminative Representation-Based Classifier for Few-Shot ...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Yuan, Tianhao Liu, Weifeng Wang, Yingjie Liu, Baodi China Univ Petr East China Qingdao 266580 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The acquisition of sufficient labeled samples is often a significant challenge in the field of remote sensing imagery, due to the time-consuming and high cost nature of field data collection. As a result, researchers have recently aimed to explore and develop effective few-shot learning methods that can overcome the shortage of labeled data in remote sensing imagery. Few-shot learning aims to enable machine learning algorithms to learn from a few labeled data or even from a single sample. The classification of remote sensing data is challenging because of the high interclass similarity and intraclass diversity found within remote sensing scenes. Direct computation of similarities between query and support data in current methods can lead to confusion. We propose a discriminative representation-based classifier (DRC) for few-shot remote sensing scene categorization to overcome this issue. Specifically, we introduce two discriminative constraint terms in the objective function: intraclass and interclass constraints. The intraclass constraint term enhances the concentration of the learned representation vectors in same class learned by the classifier, while the interclass constraint term reduces the correlation between the representation vectors of different categories. The experimental findings on the difficult remote sensing datasets NWPU-RESISC45 and RSD46-WHU demonstrate that our proposed DRC method delivers cutting-edge results in few-shot remote sensing scene image classification.

关键词： Few-shot learning remote sensing scene classification Representation-based classifier

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SFFAFormer: An Semantic Fusion and Feature Accumulation Approach for remote sensing image Change Detection 7th

SFFAFormer: An Semantic Fusion and Feature Accumulation Appr...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Hong, Yile Liu, Xiangfu Chen, Mingwei Pang, Yan Huang, Teng Wei, Bo Lang, Aobo Zhang, Xi Guangzhou Univ Inst Artificial Intelligence Guangzhou Peoples R China Beihang Univ Beijing Peoples R China Sun Yat Sen Univ Sch Art Guangzhou Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The change detection task of remote sensing images provides an effective means and technology to detect changes on the Earth's surface, providing data support for disaster management. Although current methods mostly adopt hierarchical structures and variations of transformer-base models, they overlook the rich detailed features provided by shallow layers during the restoration process, as well as the accurate global features of deep layers, leading to the loss of edge details in the final change detection structure. As a solution to this problem, we suggest SFFAFormer, which employs a module design with enhanced channel learning in shallow layers to enhance edge details and feature transfer, and utilizes transformer-base modules with semantic accumulation computation in deep layers to ensure the accuracy of global information. Experimental results demonstrate that SFFAFormer surpasses many leading baselines and achieves outstanding performance on the LEVIR-CD and DSIFN-CD datasets.

关键词： Change Detection remote sensing Semantic Fusion

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Motion ship tracking method based on ship wake recognition in GF-4 remote sensing images 6

Motion ship tracking method based on ship wake recognition i...

引用

6th International conference on Geoscience and remote sensing Mapping, GRSM 2024

作者： Qi, Wenping Wang, Wenyu Huang, Shusong Shi, Xiaojin Li, Liutong China Centre for Resources Satellite Data and Application No. 5 Fengxian East Road Beijing100094 China

ISBN: (纸本)9781510688018

To achieve continuous monitoring and tracking of ships over a long time series, a method based on multi-temporal remote sensing images from GF-4 satellite for continuous monitoring and tracking of ship wakes is provided. This method first constructs a GF-4 satellite remote sensing dataset for ship wake recognition, performs texture enhancement processing on the remote sensing images, and finally trains the model based on the YOLOv5x network. Experimental results show that image texture enhancement can significantly improve the recognition effect, and the highest detection rate can reach 91% in cloudless and fragmented cloud environments. Through testing, the single-band imaging mode can be used for continuous tracking and monitoring of multiple targets in the same area, obtaining motion information such as target position, heading, speed, and trajectory. © 2025 SPIE.

关键词： image texture

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Hyperspectral image Change Detection via Cross-Sample Slot Attention and Dual Gated Feed-Forward Network 7th

Hyperspectral Image Change Detection via Cross-Sample Slot A...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Cheng, Luyao Lin, Junyan Gao, Feng Ocean Univ China Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9789819784929;9789819784936

Hyperspectral image (HSI) change detection is a key research topic in the field of remote sensing. Existing HSI change detection methods often overlook the potential interactions among training samples. To address this issue, we develop a novel HSI change detection network, the Cross-Sample Slot attention-based Network (CSSNet). This network, building on the slot attention mechanism, can explicitly distinguish between changed and unchanged region representations and disentangle these representations by multiple independent concepts. These concepts are instrumental in capturing the uniformity and diversity in the representations among different samples during batch processing. Furthermore, we introduced a Dual Gated Feed-forward Network (DGFN) to effectively filter out redundant and irrelevant information. Experimental results on two different HSI datasets demonstrate that CSSNet outperforms several existing mainstream methods in performance.

关键词： Hyperspectral image Change Detection Cross-Sample Slot Attention

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DICMNet: Dynamic Irregular Resnet with Multi-direction Channel Remapping for remote sensing Road Extraction 7th

DICMNet: Dynamic Irregular Resnet with Multi-direction Chann...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Li, Bowen Xiao, Feng Liu, Siyu Shen, Chao Xian Technol Univ Sch Comp Sci & Engn Xian 710021 Peoples R China Xian Technol Univ Sch Weap Sci & Technol Xian 710021 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

Road information construction is the key support to solve urban traffic problems. The technique of segmenting the road part from the image is called road extraction. Therefore, road extraction can be used as a technical support for traffic system to obtain a large amount of road information. Due to the advantages of high resolution and overhead shooting angle for remote sensing image, it is very suitable as the data source of road extraction. Although road extraction from remote sensing images becomes one of the important research directions and some achievements have been made, there are still many challenges. Different from common objects, roads have strong topological continuity and are also composed of many irregular curves. This special morphological feature is difficult to be mined by common convolution kernel. To solve the above problems, this paper proposes a novel feature extraction backbone network DICMNet, which is composed of dynamic irregular convolutional (DIC) module and multi-directional channel remapping (MDCR) combine with resnet. Experimental validation with the other methods is performed on the DeepGlobe road dataset. Our model has a significant improvement on most of the evaluation metrics with an average improvement of 2%.

关键词： remote sensing road extraction dynamic irregular convolution multi-direction channel remapping

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AFSNet: Adaptive Feature Suppression Network for remote sensing image Change Detection 7th

AFSNet: Adaptive Feature Suppression Network for Remote Sens...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Li, Yang Wang, Liejun Xinjiang Univ Sch Comp Sci & Technol Urumqi Peoples R China

ISBN: (纸本)9789819785018;9789819785025

Recently, deep learning has made great progress in remote sensing change detection. However, some interference information are involved in bitemporal images which causes the algorithms to be affected by pseudo-changes in the background, such as shooting angle, seasonal turnover, and illumination intensity. Some researchers try to use the attention mechanism to solve this issue. To date, the existing attention methods explore incompletely the potentiality of feature suppression. Unlike existing spatial attention methods, we hope to obtain the interested features while removing some irrelevant-task features. From this perspective, we propose a new change detection architecture, i.e. adaptive feature suppression network (AFSNet), which includes two core components: adaptive feature suppression attention (AFSA) module and spatial and channel feature fusion (SCFF) strategy. We carefully design the AFSA inspired by soft threshold function, and it only uses 10 parameters to suppress interference information. Specifically, we remove spatial irrelevant information in the calculated process of soft threshold function and introduce a set of scaling factors to restrain redundant channel features. SCFF is an effective feature fusion strategy, and it utilizes simultaneously learnable addition and concatenation operations to aggregate better bitemporal features. Compared with some state-of-the-art (SOTA) methods on two challenging remote sensing change detection datasets, ASFNet can achieve superior performance. The code will be publicly available at https://***/tlyslll/AFSNet.

关键词： Change detection attention mechanisms adaptive feature suppression attention dual-branch feature fusion

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ML-SCODNet: Multitask Learning for Scene Classification and Object Detection Network from remote sensing images 7th

ML-SCODNet: Multitask Learning for Scene Classification and ...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Khaled, Khaled Fan, Shuyu Lian, Yuanfeng China Univ Petr Coll Artificial Intelligence Beijing 102249 Peoples R China China Univ Petr Dept Comp Sci & Technol Beijing Key Lab Petr Data Min Beijing 102249 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

This paper presents a novel and efficient multitask learning for scene classification and object detection from remote sensing images, which mainly contains the Non-Independent and Identically Distributed Fisher Vector Network (NIID-FVNet) for scene classification and Two-Stream CNN-Transformer Network (TSCTNet) for object detection. NIID-FVNet is designed to classify the input image into inshore or offshore scene. Then, TSCTNet is proposed for extracting more informative salient and edge features, in which a three-stream decoder consisting of a saliency stream, edge stream and feature fusion stream is introduced to enhance the detection result by taking full advantage of the extracted information from different modalities. Finally, the salient-aware module and edge-aware module are designed to generate more accurate saliency detection results with clear boundaries. Experimental results demonstrate that the presented scene classification and object detection networks outperforms other approaches.

关键词： Multitask learning Scene classification Object detection remote sensing Non-independent and identically distributed

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EHAT:Enhanced Hybrid Attention Transformer for remote sensing image Super-Resolution 7th

EHAT:Enhanced Hybrid Attention Transformer for Remote Sensin...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Wang, Jian Xie, Zexin Du, Yanlin Song, Wei Shanghai Ocean Univ Coll Informat Technol Shanghai Peoples R China

ISBN: (纸本)9789819786848;9789819786855

In recent years, deep learning (DL)-based super-resolution techniques for remote sensing images have made significant progress. However, these models have constraints in effectively managing long-range non-local information and reusing features, while also encountering issues such as gradient vanishing and explosion. To overcome these challenges, we propose the Enhanced Hybrid Attention Transformer (EHAT) framework, which is based on the Hybrid Attention Transformer (HAT) network backbone and combines a region-level nonlocal neural network block and a skip fusion network SFN to form a new skip fusion attention group (SFAG). In addition, we form a Multi-attention Block (MAB) by introducing spatial frequency block (SFB) based on fast Fourier convolution. We have conducted extensive experiments on Uc Merced, CLRS and RSSCN7 datasets. The results show that our method improves the PSNR by about 0.2 dB on Uc Mercedx4.

关键词： Vison Transformer remote sensing self attention super resolution Nonlocal neural Network

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Adaptive Enhanced Reversible Flow Model for remote sensing image Super Resolution 27th

Adaptive Enhanced Reversible Flow Model for Remote Sensing I...

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27th International conference on pattern recognition, ICPR 2024

作者： Li, Peishan Zhang, Yonghong Wang, Junfei Ma, Guangyi Yuan, Ziwei Nanjing University of Information Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9783031781186

In recent years, convolutional neural networks (CNNs) have excelled in remote sensing image super-resolution reconstruction (RSISR) tasks, becoming the predominant algorithms in this domain. However, these models primarily leverage the dependency of high-resolution (HR) images on low-resolution (LR) counterparts during the super-resolution (SR) forward process, neglecting mutual dependencies. To address the ill-posed nature of one-to-many mappings and enhance reconstruction performance, this paper proposes Adaptive Enhanced Reversible Flow Model (AERNet), an image SR algorithm based on invertible neural networks. AERNet treats image degradation and reconstruction as invertible transformations, where LR and HR images mutually project into each other's spaces. This mutual dependency optimizes distribution mapping across LR and HR images, constraining the solution space effectively in both forward and inverse directions. Integrating a multi-path adaptive feature fusion group and a global interaction enhancement module enhances the network's adaptability, improving its capability to fuse and enhance feature information. This approach enables more accurate processing of key image details and regions. Experimental results demonstrate AERNet's superior performance on two benchmark remote sensing datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Chareption: Change-Aware Adaption Empowers Large Language Model for Effective remote sensing image Change Captioning 7th

Chareption: Change-Aware Adaption Empowers Large Language Mo...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Wang, Changhe He, Ningyu Wang, Binglu Beijing Informat Sci & Technol Univ Sch Comp Sci Beijing Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian 710055 Peoples R China Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

remote sensing image Change Captioning (RSICC) faces significant challenges in effectively identifying and articulating changes between bi-temporal images. Traditional approaches often utilize individual text decoders, which may not capture the subtleties of visual changes nor fully exploit advanced language modeling capabilities. To overcome these limitations, we propose Change-Aware Adaption, namely Chareption, a novel framework that effectively leverages pre-trained large language models (LLMs) to enhance both the accuracy and detail of change captions. Central to Chareption is a change-aware module designed to selectively identify and utilize tokens that significantly represent changes, thus avoiding the common issue of redundancy that plagues methods relying solely on class tokens or indiscriminate use of all patch tokens. Additionally, Chareption designs a lightweight change adapter module, seamlessly integrated into both the vision backbone and the LLM, requiring minimal learnable parameters while optimally adjusting representations for the RSICC task. Our experiments on the LEVIR-CC dataset demonstrate that Chareption significantly outperforms existing methods in caption accuracy and contextual relevance, while also reducing training overhead. This establishes Chareption as a pioneering solution that sets a new direction in RSICC by harnessing the rich representational power of LLMs for improved multimodal understanding.

关键词： remote sensing image Change Caption Change-Aware Module Change Adapter Large Language Model

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