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SFDE-net: A spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

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SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

One-shot Generative Domain Adaptation in 3D GANs

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arXiv 2024年

作者： Li, Ziqiang Wu, Yi Wang, Chaoyue Rui, Xue Li, Bin Nanjing University of Information Science and Technology Nanjing China University of Science and Technology of China Hefei China University of Sydney Australia CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first considers a novel task known as One-shot 3D Generative Domain Adaptation (GDA), aimed at transferring a pre-trained 3D generator from one domain to a new one, relying solely on a single reference image. One-shot 3D GDA is characterized by the pursuit of specific attributes, namely, high fidelity, large diversity, cross-domain consistency, and multi-view consistency. Within this paper, we introduce 3D-Adapter, the first one-shot 3D GDA method, for diverse and faithful generation. Our approach begins by judiciously selecting a restricted weight set for fine-tuning, and subsequently leverages four advanced loss functions to facilitate adaptation. An efficient progressive fine-tuning strategy is also implemented to enhance the adaptation process. The synergy of these three technological components empowers 3D-Adapter to achieve remarkable performance, substantiated both quantitatively and qualitatively, across all desired properties of 3D GDA. Furthermore, 3D-Adapter seamlessly extends its capabilities to zero-shot scenarios, and preserves the potential for crucial tasks such as interpolation, reconstruction, and editing within the latent space of the pre-trained generator. Code will be available at https://***/iceli1007/3D-Adapter. © 2024, CC BY.

关键词： Generative adversarial networks

aiWave: Volumetric Image Compression with 3-D Trained Affine Wavelet-like Transform

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arXiv 2022年

作者： Xue, Dongmei Ma, Haichuan Li, Li Liu, Dong Xiong, Zhiwei The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly used volumetric image compression methods are based on wavelet transform, such as JP3D. However, JP3D employs an ideal, separable, global, and fixed wavelet basis to convert input images from pixel domain to frequency domain, which seriously limits its performance. In this paper, we first design a 3-D trained wavelet-like transform to enable signal-dependent and non-separable transform. Then, an affine wavelet basis is introduced to capture the various local correlations in different regions of volumetric images. Furthermore, we embed the proposed wavelet-like transform to an end-to-end compression framework called aiWave to enable an adaptive compression scheme for various datasets. Last but not least, we introduce the weight sharing strategies of the affine wavelet-like transform according to the volumetric data characteristics in the axial direction to reduce the number of parameters. The experimental results show that: 1) when cooperating our trained 3-D affine wavelet-like transform with a simple factorized entropy coding module, aiWave performs better than JP3D and is comparable in terms of encoding and decoding complexities;2) when adding a context module to remove signal redundancy further, aiWave can achieve a much better performance than HEVC. Copyright © 2022, The Authors. All rights reserved.

关键词： Image compression

spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020

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arXiv 2021年

作者： Liu, Chang Tang, Hairong Ji, Luyan Zhao, Yongchao Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

Miyun Reservoir has produced huge benefits in flood control, agricultural irrigation, power generation, aquaculture, tourism, and urban water supply. Accurately water mapping is of great significance to the ecological environment monitoring of the Miyun Reservoir and the management of the South-to-North Water Diversion Project. On the 60th anniversary of the completion of the Miyun Reservoir, we took the Miyun Reservoir as the study area and collected all the Landsat-5 and Landsat-8 remote sensing images from 1984 to 2020 for water mapping. Based on the spectral, topographical and temporal-spatial characteristics of water, we proposed an automated method for long-term researvoir mapping, which can solve the problems caused by cloud, shadow, ice and snow pixels. Moreover, it can also deal with the "same objects with different spectra" and spectral mixed problems. The overall accuracy is as high as 98.2% for the case with no cloud or snow/ice cover. The landscape division index is introduced to analyze the morphological changes of Miyun Reservoir. Based on the mapping results, it is suggested that the water area change of the reservoir can be divided into five periods: "growth period" (1984-1993), "peak period" (1994-1999), "decline period" (2000-2003), "protection period" "(2004-2014), and "recovery period" (2015-2020), during which the east and west areas of the reservoir have experienced the process of "division-combination-division-combination". The largest reservoir area reached 151.6 km2 (1995), and the smallest area was only 57.3 km2 (2004). Moreover, we also find that the largest area in a year occurs from August to September, and the smallest area occurs in May. In addition, we have found that the changes of water area of Miyun Reservoir are closely connected to urban development, government policies and precipitation varitations. The study shows that our method used here are potentially extendable to other reservoirs for long-term area monitoring. © 2021, CC

关键词： Time series analysis

A LINEARLY CONVERGENT METHOD FOR SOLVING HIGH-ORDER PROXIMAL OPERATOR∗

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arXiv 2023年

作者： Jingyu, G.A.O. Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences China

Recently, various high-order methods have been developed to solve the convex optimization problem. The auxiliary problem of these methods shares the general form that is the same as the high-order proximal operator proposed by Nesterov. In this paper, we present a linearly convergent method to solve the high-order proximal operator based on the classical proximal operator. In addition, some experiments are performed to demonstrate the performance of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

HIGH-ORDER PROXIMAL POINT ALGORITHM FOR THE MONOTONE VARIATIONAL INEQUALITY PROBLEM AND ITS application

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arXiv 2023年

作者： Gao, Jingyu Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences China

The proximal point algorithm (PPA) has been developed to solve the monotone variational inequality problem. It provides a theoretical foundation for some methods, such as the augmented Lagrangian method (ALM) and the alternating direction method of multipliers (ADMM). This paper generalizes the PPA to the pth-order (p ≥ 1) and proves its convergence rate O(1/kp/2). Additionally, the pth-order ALM is proposed based on the pth-order PPA. Some numerical experiments are presented to demonstrate the performance of the pth-order ALM. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

Detect Any Shadow: Segment Anything for Video Shadow Detection

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arXiv 2023年

作者： Wang, Yonghui Zhou, Wengang Mao, Yunyao Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to consider shadows as background and therefore does not perform segmentation on them. In this paper, we propose ShadowSAM, a simple yet effective framework for fine-tuning SAM to detect shadows. Besides, by combining it with long short-term attention mechanism, we extend its capability for efficient video shadow detection. Specifically, we first fine-tune SAM on ViSha training dataset by utilizing the bounding boxes obtained from the ground truth shadow mask. Then during the inference stage, we simulate user interaction by providing bounding boxes to detect a specific frame (e.g., the first frame). Subsequently, using the detected shadow mask as a prior, we employ a long short-term network to learn spatial correlations between distant frames and temporal consistency between adjacent frames, thereby achieving precise shadow information propagation across video frames. Extensive experimental results demonstrate the effectiveness of our method, with notable margin over the state-of-the-art approaches in terms of MAE and IoU metrics. Moreover, our method exhibits accelerated inference speed compared to previous video shadow detection approaches, validating the effectiveness and efficiency of our method. The source code is now publicly available at https://***/harrytea/Detect-AnyShadow. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

Continual Semantic Segmentation via Mask-Based Class Rebalancing

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Continual Semantic Segmentation via Mask-Based Class Rebalan...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongjie Guo Siya Chen Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Continual semantic segmentation (CSS) has risen as a popular field, which aims to acquire new skills constantly without forgetting past knowledge catastrophically. In CSS, we identify that there is a severe imbalance between new classes and old classes, leading to the classifier weight toward new classes. In this paper, we deal with the continual semantic segmentation problem from the class imbalance perspective via mask-based class rebalancing, avoiding the model suffering from catastrophic forgetting. More specifically, the mask-based class rebalancing depends on a mask to combine resampling with reweighting ingenuously, which mitigates the classifier bias toward new classes. Besides, we also propose a frequency knowledge distillation, leveraging multiple frequency components information to maintain the feature representation space for old classes. We demonstrate the effectiveness of our approach with an extensive evaluation of the Pascal-VOC 2012 and ADE20K datasets, significantly outperforming the state-of-the-art method.

关键词： Semantic segmentation Semantics

From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection

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arXiv 2021年

作者： Deng, Jiajun Zhou, Wengang Zhang, Yanyong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China The Department of Computer Science University of Science and Technology of China Hefei230026 China

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H23D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed H23D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at https://***/djiajunustc/H-23D_R-CNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition