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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXVII"

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image and signal processing for remote sensing xxvii

Image and Signal Processing for Remote Sensing XXVII

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image and signal processing for remote sensing xxvii 2021

ISBN: (纸本)9781510645684

The proceedings contain 33 papers. The topics discussed include: stereo matching of remote sensing images using deep stereo matching;object detection with noisy annotations in high-resolution remote sensing images using robust EfficientDet;few shot object detection in remote sensing images;fire segmentation using a squeezesegv2;deep-learning-based remote sensing video super-resolution for Jilin-1 satellite;useable machine learning for Sentinel-2 multispectral satellite imagery;self-supervised multi-task learning for semantic segmentation of urban scenes;impact of different compression rates for hyperspectral data compression based on a convolutional autoencoder;and hyperspectral image classification using spectral-spatial hypergraph convolution neural network.

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MVITP: MULTI-VIEW image-TEXT PERCEPTION FOR FEW-SHOT remote sensing image CLASSIFICATION 49

MVITP: MULTI-VIEW IMAGE-TEXT PERCEPTION FOR FEW-SHOT REMOTE ...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yang, Chen Liu, Tongtong Jiao, Didi Li, Wenhui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Few-shot learning has been extensively applied in current remote sensing image classification, enabling rapid identification of new classes by leveraging prior knowledge effectively. However, current methods mainly rely on image modality to address the issue of low intra-class similarity and high interclass similarity, while the utilization of multimodal methods in remote sensing tasks remains largely unexplored. Therefore, we propose a novel framework for few-shot remote sensing image classification, named multi-view image-text perception (MVITP). Specifically, it leverages maximum mutual information across multiple views to train an image encoder and generate image features. A text encoder is employed to generate text features. Next, we introduce a multimodal fusion encoder to capture the similarity between image features and text features. Finally, class predictions are further made by computing the similarity between the support set and the query set. We conduct experiments on three remote sensing datasets, demonstrating the outstanding performance of MVITP.

关键词： Maximum mutual information multimodal fusion few-shot learning remote sensing image classification

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A Multi-Source remote sensing image Matching Approach Based on Texture-Enhanced Region Features

A Multi-Source Remote Sensing Image Matching Approach Based ...

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9th International conference on signal and image processing (ICSIP)

作者： Zhao Zilu Wang Feng You Hongjian Li Peifeng Zhang Tingtao Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Key Lab Technol Geospatial Informat Proc & Applic Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350920

Multi-source remote sensing image matching is crucial for remote sensing technology applications. However, the variations in factors such as grayscale, perspective, and sensors between multi-source images present certain challenges for image matching. In response to the challenges in matching multi-source remote sensing images, a matching method based on texture-enhanced region features is proposed. Initially, Gabor filters and the gray-level co-occurrence matrix (GLCM) are used to obtain the texture energy maps, followed by the extraction of maximally stable extremal regions (MSER) on the texture energy maps to acquire region features. Subsequently, the contour descriptors of the features are computed using Fourier descriptors. Finally, feature matching and refinement of the matching results are conducted in conjunction with the fast sample consensus (FSC). We conducted experimental region feature matching on multiple pairs of multi-source remote sensing images, and the results validate the effectiveness of our method.

关键词： feature region matching remote image match maximally stable extremal regions (MSER) Fourier descriptors

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UNSUPERVISED remote sensing HAZE REMOVAL BASED ON SALIENCY-GUIDED TRANSMISSION REFINEMENT 49

UNSUPERVISED REMOTE SENSING HAZE REMOVAL BASED ON SALIENCY-G...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zheng, Ruohui Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Haze causes information loss and quality degradation in remote sensing images. Unsupervised learning-based dehazing methods aim to reduce reliance on paired hazy images and their labels. However, complex mapping relationships often increase the difficulty in network convergence, resulting in color distortion and loss of texture details in remote sensing images. To address these issues, we propose an unsupervised haze removal method based on saliency-guided transmission refinement for remote sensing images. Firstly, we introduce a saliency-guided transmission refinement method, which decomposes and recombines two transmission maps obtained under different conditions, guided by saliency information. Secondly, we propose a loss function comprising energy loss and texture loss. The energy loss provides an energy reference based on the coarse transmission estimation, while the texture loss enhances the preservation of texture details. Experimental results demonstrate that our method achieves comparable performance to several supervised methods.

关键词： image restoration image enhancement remote sensing dehazing unsupervised learning

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SEMANTIC SEGMENTATION FOR MULTI-SCENE remote sensing imageS WITH NOISY LABELS BASED ON UNCERTAINTY PERCEPTION 49

SEMANTIC SEGMENTATION FOR MULTI-SCENE REMOTE SENSING IMAGES ...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lyu, Xinran Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

As the annotation of remote sensing images requires domain expertise, it is difficult to construct a large-scale and accurate annotated dataset. image-level annotation data learning has become a research hotspot. In addition, due to the difficulty in avoiding mislabeling, label noise cleaning is also a concern. In this paper, a semantic segmentation method for remote sensing images based on uncertainty perception with noisy labels is proposed. The main contributions are three-fold. First, a label cleaning method based on iterative learning is presented to handle noise labels such as missing or incorrect annotations. Second, a two-stage semantic segmentation model is proposed for image-level annotation, which eliminates the need for post-processing steps during testing. Lastly, a complementary uncertainty perception function is introduced to improve the utilization of dataset features and enhance the accuracy of segmentation. The effectiveness of this method was verified through comprehensive evaluation with 7 models on four datasets.

关键词： remote sensing weak annotation label noise cleaning iterative learning uncertainty perception

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ENCODER-MINIMAL AND DECODER-MINIMAL FRAMEWORK FOR remote sensing image DEHAZING 49

ENCODER-MINIMAL AND DECODER-MINIMAL FRAMEWORK FOR REMOTE SEN...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Chen, Ting Changan Univ Sch Informat Engn Xian Peoples R China Australian Natl Univ Coll Engn & Comp Sci Canberra ACT Australia

ISBN: (纸本)9798350344868;9798350344851

Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote sensing image dehazing. Specifically, regarding the process of merging features within the same level, we develop an innovative module called intra-level transposed fusion module (ITFM). This module employs adaptive transposed self-attention to capture comprehensive context-aware information, facilitating the robust context-aware feature fusion. Meanwhile, we present a cross-level multi-view interaction module (CMIM) to enable effective interactions between features from various levels, mitigating the loss of information due to the repeated sampling operations. In addition, we propose a multi-view progressive extraction block (MPEB) that partitions the features into four distinct components and employs convolution with varying kernel sizes, groups, and dilation factors to facilitate view-progressive feature learning. Extensive experiments demonstrate the superiority of our proposed RSHazeNet. We release the source code and all pre-trained models at https://***/chdwyb/RSHazeNet.

关键词： image dehazing remote sensing image computer vision efficient network

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TWO-STAGE TRIPLETNET: LIGHT WEIGHT remote sensing SCENE CLASSIFICATION 31

TWO-STAGE TRIPLETNET: LIGHT WEIGHT REMOTE SENSING SCENE CLAS...

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2024 International conference on image processing

作者： Hu, Xianbin Wu, Wei Li, Zhu Luo, Xueliang Chen, Zhengfeng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Kansas City Dept Comp Sci Elect Engn Kansas City MO USA

ISBN: (纸本)9798350349405;9798350349399

remote sensing scene classification (RSSC) seeks to allocate correct semantic labels to remote sensing images. Recently, numerous algorithms have made significant contributions to enhancing the accuracy of RSSC. However, models with high parameters and computational complexity still dominate. To address this issue, we propose a lightweight network architecture, namely Two-Stage TripletNet. In this proposed algorithm, we employ a two-stage optimizing strategy involving label optimization and loss function optimization. First, a KD-tree generated by remote sensing image features is utilized to produce visual labels. Secondly, we establish the triplet sampling method based on the visual and semantic labels of the images. Finally, the triplet loss and cross-entropy loss are jointly applied to train our model. Experimental results on mainstream datasets demonstrate the effectiveness of our proposed framework. Meanwhile, the two-stage optimizing strategy renders our model more competitive compared to other state-of-the-art algorithms.

关键词： remote sensing Scene Classification Classification Accuracy Model Complexity Visual Label Triplet Loss

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DiffRS: An Extensible Diffusion Model for remote sensing image Generation

DiffRS: An Extensible Diffusion Model for Remote Sensing Ima...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Huang, Xinyue Niu, Xin Jiang, Jingfei Pan, Hengyue National University of Defense and Technology Changsha China

ISBN: (纸本)9798350368741

remote sensing image generation is of great value for virtual environment creation and adversarial learning for fake news detection. It could also address the learning sample shortage in the region of interest. However, most current image generation methods are limited to producing images of fixed sizes, few studies on extensible natural image generation largely focus on the stitching of random contents, lacking effective exploration of contextual information, which weakens the coherence of the extended images. To address this problem, we propose an extensible generation method for remote sensing images with the model DiffRS. This approach allows for sequential extension of arbitrary sizes by exploring the generated neighboring regions. The method is particularly suitable for scenes like remote sensing images where a generation block could cover multiple independent targets, rather than natural image tasks which may stitch across regions to form a completely target. Compared to the state-of-the-art extensible generation methods, DiffRS could improve the large scale image generation with better structure consistency, richer details and higher realism. Experiments showed that DiffRS could improve the FID score by 4.6% and 3.2% respectively in comparison with the MultiDiffusion and Mixture of Diffuser models. © 2025 IEEE.

关键词： Diffusion Model image Extensible Generation remote sensing image Sequence Generation

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CLOUDS AND HAZE CO-REMOVAL BASED ON WEIGHT-TUNED OVERLAP REFINEMENT DIFFUSION MODEL FOR remote sensing imageS 31

CLOUDS AND HAZE CO-REMOVAL BASED ON WEIGHT-TUNED OVERLAP REF...

引用

2024 International conference on image processing

作者： Zhang, Jingxuan Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

remote sensing image dehazing is essential for preprocessing, but most methods overlook the joint occlusion by clouds and haze. Furthermore, generative model-based restoration struggles with haze, weakening edge recovery for targets obscured by both elements. In this paper, we propose a new diffusion model based on weight-tuned overlap refinement for clouds and haze co-removal in remote sensing images. Firstly, we integrate the diffusion model into clouds and haze co-removal task, offering a lightweight solution effective even under limited samples. Secondly, we propose a weight-tuned overlap refinement method (WTOR) to guide noise estimation updating in overlaps in the backward-sampling process. It takes the reciprocal of the variance of each overlap as the weight factor, adjusts the backward-sampling update frequency to improve the model's ability of clouds and haze co-removal. Finally, we introduce the gaussian filter feature extraction block (GFAB) to enhance the learning ability of the model for structural information, which can detect local changes and extract features with precision. The experimental results demonstrate that the proposed method is capable of co-removing clouds and haze with reserving rich color and texture details.

关键词： image enhancement remote sensing image dehazing cloud removal diffusion model

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HAZY remote sensing imageS SEMANTIC SEGMENTATION FOR WEAKLY ANNOTATION BASED ON SALIENCY-AWARE ALIGNMENT STRATEGY 49

HAZY REMOTE SENSING IMAGES SEMANTIC SEGMENTATION FOR WEAKLY ...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xu, Junda Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

The technique of semantic segmentation (SS) holds significant importance in the domain of remote sensing image (RSI) processing. The current research primarily encompasses two problems: 1) RSIs are easily affected by clouds and haze;2) SS based on strong annotation requires vast human and time costs. In this paper, we propose a weakly supervised semantic segmentation (WSSS) method for hazy RSIs based on saliency-aware alignment strategy. Firstly, we design alignment network (AN) and target network (TN) with the same structure. After training the AN with clear images, we extract the class activation maps of the two networks and construct a consistency loss to train the TN with hazy images. Secondly, we design a multi-scale channel-spatial attention module in the two classification networks to solve the problems of unclear foreground-background boundary and blurred target texture in hazy images. Finally, the pseudo-labels generated by the TN are utilized to train a feedback saliency analysis network, which is subsequently employed for obtaining segmentation results during the testing phase. The experiment results demonstrate that our approach achieves superior SS performance for hazy RSIs.

关键词： Semantic segmentation remote sensing image weakly annotation image dehazing saliency analysis

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