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检索条件"任意字段=5th IAPR Workshop on Pattern Recognition in Remote Sensing"

共 155 条记录，以下是1-10 订阅

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Image Augmentations in Planetary Science: Implications in Self-Supervised Learning and Weakly-Supervised Segmentation on Mars 26

Image Augmentations in Planetary Science: Implications in Se...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Kossmann, Dominik Matei, Arthur Wilhelm, thorsten Fink, Gernot A. TU Dortmund Univ Dept Comp Sci Dortmund Germany TU Dortmund Univ Dept Elect Engn & Informat Technol Dortmund Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Research on the use of augmentations in physically constrained remote sensing scenarios, like the analysis of Martian surface data, is largely unexplored. In this work we present an analysis on how reasonable augmentation strategies can be selected which are class agnostic and respect physical plausibility in supervised and weakly-supervised tasks. Additionally, we present the first results of self-supervised learning on Martian surface data, discuss the importance of physically plausible augmentations in the context of self-supervised learning, specifically contrastive learning, and provide a comprehensive overview of the generalization properties induced by different augmentation strategies with the help of geomorphic maps.

关键词： Mars Image segmentation Self-supervised learning pattern recognition Task analysis remote sensing

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Embedding Spatial Relations in Visual Question Answering for remote sensing 26

Embedding Spatial Relations in Visual Question Answering for...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Faure, Maxime Lobry, Sylvain Kurtz, Camille Wendling, Laurent Univ Paris Cite LIPADE F-75006 Paris France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

remote sensing images carry a wealth of information that is not easily accessible to end-users as it requires strong technical skills and knowledge. Visual Question Answering (VQA), a task that aims at answering an open-ended question in natural language from an image, can provide an easier access to this information. Considering the geographical information contained in remote sensing images, questions often embed an important spatial aspect, for instance regarding the relative position of two objects. Our objective is to better model the spatial relations in the construction of a ground-truth database of image/question/answer triplets and to assess the capacity a VQA model has to answer these questions. In this article, we propose to use histograms of forces to model the directional spatial relations between geo-localized objects. this allows a finer modeling of ambiguous relationships between objects and to provide different levels of assessment of a relation (e.g. object A is slightly/strictly to the west of object B). Using this new dataset, we evaluate the performances of a classical VQA model and propose a curriculum learning strategy to better take into account the varying difficulty of questions embedding spatial relations. With this approach, we show an improvement in the performances of our model, highlighting the interest of embedding spatial relations in VQA for remote sensing applications.

关键词： Training Visualization Histograms Feature extraction Question answering (information retrieval) Spatial databases pattern recognition

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remote sensing Image Scene Classification via Multi-Level Representation Learning 26

Remote Sensing Image Scene Classification via Multi-Level Re...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Fu, Wei Yang, Lishuang Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

remote sensing image scene classification (RSSC), which assigns semantic labels to remote sensing images, is very important for remote sensing image interpretation. thanks to the rapid development of deep learning, RSSC achieves significant breakthroughs by the use of convolutional neural network (CNN). However, CNN relies on local receptive fields and is difficult to capture long-range and global scene information. Moreover, the information of salient objects, which contributes to discriminate the category of scenes (e.g., airplanes indicate the airport scene), should be also exploited. To address this issue, a deep learning method, named multi-level representation learning (MLRL), is proposed to collaboratively extract pixel-level, patch-level, and object-level features, which respectively contain local, global, and object-oriented information. Specifically, pixel-level features are obtained by pixel-wise convolution operations within a CNN. Patch-level features are achieved by a patch-wise self-attention network. Object-level features are acquired by applying a CNN to a cropped sub-image, which conveys important information of salient objects. To this end, a three-branch network structure to respectively extract above features, is built. Finally, a decision fusion method is adopted to integrate multi-level features, and gives rise to refined classification results. Experiments conducted on widely-used datasets demonstrate the effectiveness of the proposed method.

关键词： Deep learning Representation learning Measurement Image analysis Fuses Semantics Feature extraction

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12th workshop on pattern recognition in remote sensing

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Photogrammetric Record 2022年第178期37卷 250-251页

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Intensity-Sensitive Similarity Indexes for Image Quality Assessment 26

Intensity-Sensitive Similarity Indexes for Image Quality Ass...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Li, Xiaotong Armour, Wesley Univ Oxford Dept Engn Sci Oxford E Res Ctr Oxford England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. therefore, we propose two new IQA methods, InTensityWeighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. the application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. the results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.

关键词： Image quality Computer vision Sensitivity Visual systems pattern recognition Indexes remote sensing

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11th iapr workshop on pattern recognition in remote sensing and 25th International Conference on pattern recognition

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Photogrammetric Record 2020年第172期35卷 554-554页

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AutoLC: Search Lightweight and Top-Performing Architecture for remote sensing Image Land-Cover Classification 26

AutoLC: Search Lightweight and Top-Performing Architecture f...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Zheng, Chenyu Wang, Junjue Ma, Ailong Zhong, Yanfei Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ LIESMARS Wuhan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution remote sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very redundant and inefficient to process HRS images. On the other hand, nascent Neural Architecture Search (NAS) techniques for dense prediction tasks are mainly based on encoder-decoder architecture, and just focus on the automatic design of the encoder, which makes it still difficult to recover the refined mapping when confronting complicated HRS scenes. To overcome their defects and tackle the HRS land-cover classification problems better, we propose AutoLC which combines the advantages of two methods. First, we devise a hierarchical search space and gain the lightweight encoder underlying gradient-based search strategy. Second, we meticulously design a lightweight but top-performing decoder that is adaptive to the searched encoder of itself. Finally, experimental results on the LoveDA land-cover dataset demonstrate that our AutoLC method outperforms the state-of-art manual and automatic methods with much less computational consumption.

关键词： Training Gradient methods Image processing Computer architecture Manuals Search problems Decoding

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A lightweight multi-scale context network for salient object detection in optical remote sensing images 26

A lightweight multi-scale context network for salient object...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Lin, Yuhan Sun, Han Liu, Ningzhong Bian, Yetong Cen, Jun Zhou, Huiyu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH Leics England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. the code will be available at https://***/NuaaYH/MSCNet.

关键词： Integrated optics Semantics Object detection Optical fiber networks Optical imaging Feature extraction Optical sensors

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Controlled Multi-modal Image Generation for Plant Growth Modeling 26

Controlled Multi-modal Image Generation for Plant Growth Mod...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Miranda, Miro Drees, Lukas Roscher, Ribana Univ Bonn IGG Remote Sensing Niebuhrstr 1a D-53113 Bonn Germany Tech Univ Munich Data Sci Earth Observat Lise Meitner Str 9 D-85521 Ottobrunn Germany German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Predicting plant development is an important task in precision farming and an essential metric for decision-making by researchers and farmers. In this work, we propose a novel generative modeling technique for plant growth prediction based on conditional generative adversarial networks. We formulate plant growth as an image-to-image translation task and predict the appearance of a plant growth stage as a function of its previous stage. We take into account that plant growth is inherently multi-modal, depending on numerous and highly variable environmental factors, and thus a single input belongs to a distribution of potential outputs. We encode the ambiguity in an interpretable and low-dimensional latent vector space representing the various factors of variation that are influencing plant growth. We use a novel encoder-based data fusion technique and combine information contained in remote sensing imagery of different cropping systems with data containing the factors of variation to adequately model plant growth. this offers several advantages over existing methods: (1) we show that we can model a distribution of potential appearances and simultaneously outperform existing methods in providing more realistic predictions, (2) the complexity of plant growth is more adequately captured, as various factors influencing plant growth can be included, (3) predictions are controllable by being conditioned by an interpretable latent vector representing the factors of variation along with an input image of a previous growth stage.

关键词： Measurement Image synthesis Ecosystems Predictive models Generative adversarial networks pattern recognition Task analysis

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Multi-View Stereo and Depth Priors Guided NeRF for View Synthesis 26

Multi-View Stereo and Depth Priors Guided NeRF for View Synt...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Deng, Wang Zhang, Xuetao Guo, Yu Lu, Zheng Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Xian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this paper, we present a new framework for view synthesis of novel view based on Neural Radiance Fields(NeRF). We aim to address two main limitations of NeRF. Firstly, we propose to combine multi-view stereo into NeRF to help construct general neural radiance fields across different scenes. Specifically, We build a MVS-Encoding Feature Volume with average group-wise correlation to aggregate the multi-view appearance and geometry feature for every source view. And then we use an MLP to encode neural radiance fields by using the scene-dependent features interpolated from the MVS-Encoding Feature Volumes. this makes our model can be applied to other unseen scenes without any per-scene fine-tuning, and render realistic images with few images. If more training images are provided, our method can be fine-tuned quickly to render more realistic images. In fine-tuning phase, we propose a depth priors guided sampling method, which can make the model represent more accurate geometry for corresponding scenes and so render high-quality images of novel view. We evaluate our method on three common datasets. the experiment results show that our method performs better than other baselines, neither without or with fine-tuning. And the depth priors guided sampling method can be easily applied on other methods based on Neural Radiance Fields to further improve the quality of rendered images.

关键词： View Synthesis Neural Radiance Fields Multi-View Stereo Depth Priors

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