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

共 4031 条记录，以下是21-30 订阅

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Detecting Out-Of-Distribution Earth Observation images with Diffusion Models

Detecting Out-Of-Distribution Earth Observation Images with ...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Le Bellier, Georges Audebert, Nicolas Cnam CEDRIC EA4629 F-75141 Paris France Univ Gustave Eiffel ENSG IGN LASTIG F-94160 St Mande France

ISBN: (纸本)9798350365474

Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically different features for these out-of-distribution samples, compared to those closer to their training dataset. Detecting them could therefore help anticipate changes in the observations, either geographical or environmental. In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images., using as a plausibility score. Moreover, we introduce ODEED, a novel reconstruction-based scorer using the probability-flow ODE of diffusion models. We validate it experimentally on SpaceNet 8 with various scenarios, such as classical OOD detection with geographical shift and near-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We show that our ODEED scorer significantly outperforms other diffusion-based and discriminative baselines on the more challenging near-OOD scenarios of flood image detection, where OOD images are close to the distribution tail. We aim to pave the way towards better use of generative models for anomaly detection in remote sensing.

关键词： deep learning diffusion models out-of-distribution detection remote sensing

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Landslide extraction model for remote sensing images based on improved DeepLabv3+

Landslide extraction model for remote sensing images based o...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Wu, Yanling Lang, Wenhui School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781510680425

In order to realize the accurate recognition of landslides in remote sensing images, an improved DeepLabv3+ landslide extraction model is proposed in this paper.(1) Hybrid Module and Attention Module based CSPNet (HA-CSPNet) is constructed as the backbone feature extraction network to enhance the feature information of small landslides and suppress the interference of irrelevant background information.(2) Combining the advantages of Residual Structure and Dense Connected Module, Residual-dense ASPP is designed to focus landslide features at different scales, enhance feature reuse and prevent gradient *** experimental results show that the landslide extraction model proposed in this paper is *** can improve the accuracy of landslide semantic segmentation. © 2024 SPIE.

关键词： Semantic Segmentation

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GeoChat : Grounded Large Vision-Language Model for remote sensing

GeoChat : Grounded Large Vision-Language Model for Remote Se...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Kuckreja, Kartik Danish, Muhammad Sohail Naseer, Muzammal Das, Abhijit Khan, Salman Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Birla Inst Technol & Sci Hyderabad India Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly for remote sensing (RS) scenarios, leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example, to handle high-resolution RS imagery with diverse scale changes across categories and many small objects, region-level reasoning is necessary alongside holistic scene interpretation. Furthermore, the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries. To address these limitations, we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically, GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Furthermore, it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets, we generate a novel RS multi-modal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. We establish a comprehensive benchmark for RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various RS tasks, e.g., image and region captioning, visual question answering, scene classification, visually grounded conversations and referring detection. Our code is available here.

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A novel CNN-Transformer network for cloud detection in remote sensing image

A novel CNN-Transformer network for cloud detection in remot...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Ai, Xinkai Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Shandong Province Qingdao266590 China

ISBN: (纸本)9781510680425

Convolutional neural networks (CNNs) are the mainstream model for extracting rich features in deep learning-driven studies on cloud detection for remote sensing images. However, due to the limitation of receptive fields in convolutional operations, a single CNN has limitations in mining global information and long-range dependencies, which affects the precision of cloud identification in intricate environments. To address the insufficient global feature and long-range dependency modeling of CNNs, a new CNN-Transformer network for cloud detection (CNN-TFCD) in remote sensing images is proposed in this paper. This method comprehensively utilizes strengths of both CNN and transformer to fully mine local spatial details, global contextual information, and long-range dependencies, to obtain comprehensive feature representations. The feature cascade mechanism is applied for multi-scale feature fusion, further enhancing the model's feature representation capability. Evaluations conducted on the Landsat-8 dataset demonstrate that the CNN-TFCD method achieves accurate and stable cloud detection. © 2024 SPIE.

关键词： Convolutional neural networks

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Rethinking Transformers Pre-training for Multi-Spectral Satellite imagery

Rethinking Transformers Pre-training for Multi-Spectral Sate...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Noman, Mubashir Naseer, Muzammal Cholakkal, Hisham Anwar, Rao Muhammad Khan, Salman Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets, remote sensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remote sensing imagery or restrict themselves to use only a single type of data modality. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach, named SatMAE++, performs multiscale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works, the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions, leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset. Our code and pre-trained models are available at https://***/techmn/satmae_pp.

关键词： multi-scale mask auto encoder multi-spectral imagery remote sensing scene classification self-supervised learning Vision Transformers

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HSDFormer: an improved transformer for remote sensing image semantic segmentation

HSDFormer: an improved transformer for remote sensing image ...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Bai, Yang Gao, Suixiang University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Zhongguancun Laboratory Beijing100049 China

ISBN: (纸本)9781510680425

The extraction of building locations is crucial in the field of remote sensing, commonly applied in tasks such as emergency response, urban planning, and environmental monitoring. Existing methods often employ convolutional models such as ResNet and U-Net. however, these models struggle to capture long-range features of geographical objects, limiting their practical effectiveness. Meanwhile, Transformer based models face challenges due to their quadratic computational complexity. Additionally, the characteristics of remote sensing images, including dense arrangement, small scale, and class imbalance, pose difficulties for traditional patch-merging modules in downsampling, leading to the loss of significant building information. To address such limitations, we present the HSDFormer model in this paper. The model achieves a reduction in the computational complexity of self-attention calculation through a straightforward sequential reduction process. Additionally, our model has optimized the downsampling process specifically for remote sensing semantic segmentation tasks, effectively minimizing the loss of crucial information during layer-wise feature extraction. We evaluate the HSDFormer on the WHU Building Dataset and Satellite Dataset II (East Asia). The results demonstrate the model's effectiveness in accurately segmenting building locations, surpassing performance compared to other baseline models. © 2024 SPIE.

关键词： Semantic Segmentation

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Content-Adaptive Non-Local Convolution for remote sensing Pansharpening

Content-Adaptive Non-Local Convolution for Remote Sensing Pa...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Duan, Yule Wu, Xiao Deng, Haoyu Deng, Liang-Jian Univ Elect Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

Currently, machine learning-based methods for remote sensing pansharpening have progressed rapidly. However, existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces, thereby limiting the effectiveness of the methods and resulting in redundant learning parameters. In this paper, we introduce a so-called content-adaptive non-local convolution (CANConv), a novel method tailored for remote sensing image pansharpening. Specifically, CANConv employs adaptive convolution, ensuring spatial adaptability, and incorporates non-local self-similarity through the similarity relationship partition (SRP) and the partition-wise adaptive convolution (PWAC) sub-modules. Furthermore, we also propose a corresponding network architecture, called CANNet, which mainly utilizes the multi-scale self-similarity. Extensive experiments demonstrate the superior performance of CANConv, compared with recent promising fusion methods. Besides, we substantiate the method's effectiveness through visualization, ablation experiments, and comparison with existing methods on multiple test sets. The source code is publicly available at https://***/duanyll/CANConv.

关键词： Contrastive Learning

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Learnable Prompt for Few-Shot Semantic Segmentation in remote sensing Domain

Learnable Prompt for Few-Shot Semantic Segmentation in Remot...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Immanuel, Steve Andreas Sinulingga, Hagai Raja TelePIX Seoul South Korea

ISBN: (纸本)9798350365474

Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples. In the generalized setting, the task extends to segment both the base and the novel classes. The main challenge is how to train the model such that the addition of novel classes does not hurt the base classes performance, also known as catastrophic forgetting. To mitigate this issue, we use SegGPT as our base model and train it on the base classes. Then, we use separate learnable prompts to handle predictions for each novel class. To handle various object sizes which typically present in remote sensing domain, we perform patch-based prediction. To address the discontinuities along patch boundaries, we propose a patch-and-stitch technique by re-framing the problem as an image inpainting task. During inference, we also utilize image similarity search over image embeddings for prompt selection and novel class filtering to reduce false positive predictions. Based on our experiments, our proposed method boosts the weighted mIoU of a simple fine-tuned SegGPT from 15.96 to 35.08 on the validation set of few-shot Open-EarthMap dataset given in the challenge.

关键词： learnable prompt patch-and-stitch

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SPECTRAL-AWARE DEEP NETWORKS FOR OBJECT DETECTION IN HYPERSPECTRAL imageS WITH CLOUD INTERFERENCE 24

SPECTRAL-AWARE DEEP NETWORKS FOR OBJECT DETECTION IN HYPERSP...

引用

1st International conference on image processing Machine Learning and pattern recognition

作者： Zhang, Ying Zeng, Xia Zhang, Hongtao Xu, Meng Shenzhen Univ Shenzhen Peoples R China Beijing Inst Astronaut Syst Engn Beijing Peoples R China

ISBN: (纸本)9798400707032

Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel level, HOD remains underexplored. Traditional HTD methods rely heavily on prior spectral information of the target and simple pixel neighborhood relationships, leading to accuracy issues when targets are occluded. Inspired by advances in RGB image detection, we propose a compact and efficient cloud-robust hyperspectral object detection network (CR-HODNet) using 3D convolution to extract spatial and spectral features jointly. We further enhance these features with channel and spatial attention mechanisms and address cloud occlusion challenges using transformer-based multi-head attention. Our method is validated on real airborne hyperspectral images with synthetic cloud occlusion, showing robust performance in challenging scenarios.

关键词： Hyperspectral image Object detection Cloud interference remote sensing

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D-CANet: Diverse class-aware coding and decoding structure network for semantic segmentation of high-resolution remote sensing images

D-CANet: Diverse class-aware coding and decoding structure n...

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Yuan, Zhengwu Shao, Wen Chen, Qiang Ke, Yingqi College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing China

ISBN: (纸本)9781510680425

The substantial scale variation and intra-class diversity within remote sensing imagery pose significant challenges for semantic segmentation, rendering methods developed for natural images inapplicable. These challenges, we introduce a novel semantic segmentation model named D-CANet, which primarily comprises three modules: the Global Class Center Awareness (GCCA), the Local Class Awareness Module (LCAM), and the Global Class Generation Module (GCG). Specifically, the GCCA module is dedicated to modeling the global representation of class context to mitigate the interference from image backgrounds;the LCAM module generates a local class representation, serving as an intermediary perceptual element that facilitates an implicit linkage between pixels and global class representations, minimizing the variance within classes;following the processing by the LCAM module, the GCG module enhances the global class representation. This encoder-decoder structure equipped with GCCA, LCAM, and GCG modules achieves precise segmentation of objects of varying scales within remote sensing imagery through the interactive perception and fusion of global and local features. Experimental assessments conducted on the Potsdam dataset and the Vaihingen dataset illustrate that D-CANet surpasses the current state-of-the-art semantic segmentation techniques in terms of efficacy. © 2024 SPIE.

关键词： Semantic Segmentation

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