Query：
所有字段：(空)
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
In the semantic segmentation of high-resolution remote sensing images, utilizing the normalized Digital Surface Model (nDSM) that provides height information as auxiliary data and fusing it with the visible image can improve the accuracy of segmentation. However, the better utilization of complementarity between different modal features has not been fully explored. In this work, we propose a new dual-branch and multi-stage Bimodal Fusion Rectification Network (BFRNet), which is end-to-end trainable. It consists of three modules: Channel and Spatial Fusion Rectification (CSFR) module, Edge Fusion Refinement (EFR) module, and Multiscale Feature Fusion (MSFF) module. The CSFR module integrates and rectifies multimodal features in both channel and spatial dimensions, achieving sufficient interaction and fusion between multimodal features. The EFR module obtains better multiscale edge features than single modality through feature fusion based on bimodal interactive edge attention and spatial gate, which helps to alleviate the edge loss of ground objects in single modality. The MSFF module is used to upsample and fuse multiscale features from EFR and CSFR to generate the final semantic segmentation results. The experimental results on the two public datasets, Vaihingen and Potsdam, provided by ISPRS, showcase the comparative advantage of the proposed method over other research methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Hydrogeology Hydrogeology Image enhancement Image enhancement Jurassic Jurassic Metadata Metadata Semantic Segmentation Semantic Segmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Weng, Qian , Lin, Yifeng , Pan, Zengying et al. BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation [C] . 2025 : 501-515 . |
| MLA | Weng, Qian et al. "BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation" . (2025) : 501-515 . |
| APA | Weng, Qian , Lin, Yifeng , Pan, Zengying , Lin, Jiawen , Chen, Gengwei , Chen, Mo et al. BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation . (2025) : 501-515 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
在高分遥感图像语义分割中,为解决如何有效融合光谱信息与高程信息以分割相似光谱的不同地物的问题和通过捕获长距离依赖信息来提升局部地物识别精度,本文提出一种面向多源数据的多区域尺度协同语义分割方法.该方法包括:一种不等长的多分支语义分割网络,以有效提取多源特征,充分利用多源数据之间的互补信息;一个轻量级的协同注意力特征融合模块,用于在特征融合阶段有效地融合多分支特征;一种多区域尺度协同的数据增强方法,引导网络捕获长距离依赖信息.在ISPRS提供的公开数据集Vaihingen和Potsdam上的实验结果表明,与同类型主流方法对比,本文提出的方法具有更优的分割性能,且得到的地物细节信息更加完整,参数量也更小.
Keyword ：
协同注意力 协同注意力 多源数据融合 多源数据融合 数字表面模型 数字表面模型 语义分割 语义分割 高分遥感图像 高分遥感图像
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 林易丰 , 陈光剑 , 陈浩 et al. 面向多源数据的多区域尺度协同高分遥感图像语义分割 [J]. | 小型微型计算机系统 , 2025 , 46 (1) : 158-166 . |
| MLA | 林易丰 et al. "面向多源数据的多区域尺度协同高分遥感图像语义分割" . | 小型微型计算机系统 46 . 1 (2025) : 158-166 . |
| APA | 林易丰 , 陈光剑 , 陈浩 , 翁谦 , 林嘉雯 . 面向多源数据的多区域尺度协同高分遥感图像语义分割 . | 小型微型计算机系统 , 2025 , 46 (1) , 158-166 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
波段选择是高光谱遥感图像降维的一项重要任务,其目标是选择包含较少冗余信息、较大信息量和具有类别可分性的波段子集.为解决基于近邻子空间划分的波段选择方法没有考虑地物空间分布和计算聚类中心时忽略噪声波段影响的问题,本文提出了一种结合空谱结构与改进局部密度的高光谱图像波段选择方法.该方法首先对高光谱图像进行基于熵率的图像分割获得高光谱图像同质区域,综合同质区域相关系数矩阵获得图像区域级近邻波段相关系数向量;其次,用高斯核平滑全局近邻波段相关系数向量以降低噪声波段的影响,并根据极值点进行波段分组;然后,最大化改进局部密度和波段信息熵的乘积作为选取代表性波段的标准;最后,在Indian Pines、Botswana和Salinas高光谱图像数据集上,通过SVM、KNN和LDA分类器上进行分类实验.结果表明:(1)对比像素级相关系数划分方法,利用区域级相关系数使得近邻波段分组更为合理,降低波段冗余性,同时还保留了部分潜在特征波段,在3个数据集上的分类性能分别提高了 2.63％,0.68％,0.16％;(2)对比仅使用信息熵的波段衡量方法,本文提出的最大化改进的局部密度和信息熵乘积的方法是有效的,在3个数据集上OA分别提高了 4.13％、0.5％和0.21％;(3)对比其他6种先进波段选择方法,本文方法在3个数据集上的OA分别从62.34％提高到75.03％、86.74％提高到88.28％和86.04％提高到92.36％.此外,所选择的波段子集在分布上较为分散,主要集中在信息熵较高的区域,同时避免了选择噪声波段.综上,本文提出的波段选择方法所得波段子集具有较低的冗余性、丰富的信息量、强的类别可分性,并且对噪声具有较强的鲁棒性,能够有效地解决高光谱图像波段选择的问题.
Keyword ：
信息熵 信息熵 分类 分类 子空间划分 子空间划分 局部密度 局部密度 峰值密度 峰值密度 波段选择 波段选择 遥感 遥感 高光谱图像 高光谱图像
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 翁谦 , 安远 , 陈光剑 et al. 结合空谱结构与改进局部密度的高光谱图像波段选择 [J]. | 遥感学报 , 2025 , 29 (1) : 247-265 . |
| MLA | 翁谦 et al. "结合空谱结构与改进局部密度的高光谱图像波段选择" . | 遥感学报 29 . 1 (2025) : 247-265 . |
| APA | 翁谦 , 安远 , 陈光剑 , 吴瑞姣 , 林嘉雯 . 结合空谱结构与改进局部密度的高光谱图像波段选择 . | 遥感学报 , 2025 , 29 (1) , 247-265 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
ZY-1 02E satellite is a Chinese hyperspectral satellite launched in recent years. There are few reports on its application at present, so there is no suitable research method. In this study, rice planting area was extracted from two experimental areas of Fujian Province using Ziyuan No. 1 02E hyperspectral data. A multi-scale spectral feature extraction network Based on spatial neighborhood (MSFEN-BSN) was proposed to solve the high spectral resolution, low spatial resolution and complex and fragmented distribution of crops in the experimental area. The network consists of two modules: Multi-scale Spectral Feature Extraction (MSFE) and Spatial Neighborhood Feature Fusion (SNFF), which can extract spectral features at multiple scales to obtain deep spectral semantics, and fuse spectral-spatial information of neighboring pixels to obtain more effective classification features. The mapping accuracy of rice classification in two experimental areas reached 83.61% and 84.36% respectively, which can meet the needs of practical application. Meanwhile, to prove the robustness of the proposed method, precision verification is carried out on two public vegetation datasets Indian pines (IP) and Salinas Valley (SA). Compared with some traditional vegetation extraction methods and classical deep learning methods, the proposed method has higher precision. © 2024 Copyright held by the owner/author(s).
Keyword ：
attention mechanism attention mechanism Convolutional Neural Network Convolutional Neural Network hyperspectral image classification hyperspectral image classification rice extraction rice extraction ZY-1 02E ZY-1 02E
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Z. , Wu, R. , Chen, G. et al. Extraction of Rice Planting Range Based on Hyperspectral Data of ZY-1 02E [未知]. |
| MLA | Pan, Z. et al. "Extraction of Rice Planting Range Based on Hyperspectral Data of ZY-1 02E" [未知]. |
| APA | Pan, Z. , Wu, R. , Chen, G. , Jian, C. , Weng, Q. . Extraction of Rice Planting Range Based on Hyperspectral Data of ZY-1 02E [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Hyperspectral images can provide dozens to hundreds of continuous spectral bands, greatly enhancing the richness of information. However, the redundancy among adjacent bands leads to increased data processing complexity. Despite the recent introduction of numerous band selection methods, there has been limited focus on incorporating context information from the entire spectral range into this task. Furthermore, researchers have primarily concentrated on band informativeness and sparse representation for reconstructing all bands, often overlooking the separability of classes in downstream tasks. To address these challenges, we propose a hyperspectral band selection network combining Siamese Network Local-Global Attention (SLGA). This approach first segments the hyperspectral image into homogeneous regions and constructs sample pairs based on a random elimination strategy. Next, it utilizes the Local-Global Attention (L-GA) mechanism to obtain band weights that capture both local and global spectral structures. These reweighted bands are then fed into a twin network to obtain their high-dimensional representations, compute loss values, and update network parameters. Finally, extensive classification experiments using SVM, KNN, and LDA classifiers are conducted on the Indian Pines and Botswana hyperspectral image datasets. The results from these experiments on benchmark datasets demonstrate that the proposed SLGA method performs exceptionally well, outperforming state-of-the-art algorithms. © 2023 IEEE.
Keyword ：
Classification (of information) Classification (of information) Convolutional neural networks Convolutional neural networks Data handling Data handling Image enhancement Image enhancement Support vector machines Support vector machines
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | An, Yuan , Chen, Guangjian , Huang, Dehua et al. A Hyperspectral Band Selection Network Combining Siamese Network and Local-Global Attention [C] . 2023 : 705-710 . |
| MLA | An, Yuan et al. "A Hyperspectral Band Selection Network Combining Siamese Network and Local-Global Attention" . (2023) : 705-710 . |
| APA | An, Yuan , Chen, Guangjian , Huang, Dehua , Zheng, Huiqin , Wang, Qin , Jian, Cairen et al. A Hyperspectral Band Selection Network Combining Siamese Network and Local-Global Attention . (2023) : 705-710 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Models based on convolutional neural networks (CNNs) have achieved remarkable advances in high-resolution remote sensing (HRRS) images scene classification, but there are still challenges due to the high similarity among different categories and loss of local information. To address this issue, a multigranularity alternating feature mining (MGA-FM) framework is proposed in this article to learn and fuse both global and local information for HRRS scene classification. First, a region confusion mechanism is adopted to guide network's shallow layers to adaptively learn the salient features of distinguishing regions. Second, an alternating comprehensive training strategy is designed to capture and fuse shallow local feature information and deep semantic information to enhance feature representation capabilities. In particular, the MGA-FM framework can be flexibly embedded in various CNN backbone networks as a training mechanism. Extensive experimental results and visualization analysis on three remote sensing scene datasets indicated that the proposed method can achieve competitive classification performance.
Keyword ：
Convolutional neural network (CNN) Convolutional neural network (CNN) feature mining feature mining local detailed information local detailed information remote sensing image remote sensing image scene classification scene classification
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Weng, Qian , Huang, Zhiming , Lin, Jiawen et al. Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 : 318-330 . |
| MLA | Weng, Qian et al. "Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16 (2023) : 318-330 . |
| APA | Weng, Qian , Huang, Zhiming , Lin, Jiawen , Jian, Cairen , Mao, Zhengyuan . Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 , 318-330 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
借鉴基于正则回归的无监督并行正交基聚类特征选择法和最大互信息系数,提出正交基低冗余无监督特征选择法.该方法在正交基下选择具有判别能力的特征,可用最大互信息系数矩阵选择低冗余性的特征子集. 4个图像数据集上的实验结果表明:该方法选择的特征子集可以提高聚类准确率.
Keyword ：
低冗余 低冗余 无监督 无监督 正交基 正交基 特征选择 特征选择 聚类 聚类
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 简彩仁 , 翁谦 . 正交基低冗余无监督特征选择法 [J]. | 福州大学学报(自然科学版) , 2022 , 50 (01) : 1-8 . |
| MLA | 简彩仁 et al. "正交基低冗余无监督特征选择法" . | 福州大学学报(自然科学版) 50 . 01 (2022) : 1-8 . |
| APA | 简彩仁 , 翁谦 . 正交基低冗余无监督特征选择法 . | 福州大学学报(自然科学版) , 2022 , 50 (01) , 1-8 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
针对目前主流深度学习网络模型应用于高空间分辩率遥感影像建筑物提取存在的内部空洞、不连续以及边缘缺失与边界不规则等问题，本文在U-Net模型结构的基础上通过设计新的激活函数（ACON）、集成残差以及通道-空间与十字注意力模块，提出RMAU-Net模型。该模型中的ACON激活函数允许每个神经元自适应地激活或不激活，有利于提高模型的泛化能力和传输性能；残差模块用于拓宽网络深度并降低训练和学习的难度，获取深层次语义特征信息；通道-空间注意力模块用于增强编码段与解码段信息的关联、抑制无关背景区域的影响，提高模型的灵敏度；十字注意力模块聚合交叉路径上所有像素的上下文信息，通过循环操作捕获全局上下文信息，提...
Keyword ：
ACON激活函数 ACON激活函数 十字注意力 十字注意力 卷积神经网络 卷积神经网络 建筑物提取 建筑物提取 残差块 残差块 空间注意力 空间注意力 通道注意力模块 通道注意力模块 高分影像 高分影像
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 吴新辉 , 毛政元 , 翁谦 et al. 利用基于残差多注意力和ACON激活函数的神经网络提取建筑物 [J]. | 地球信息科学学报 , 2022 , 24 (04) : 792-801 . |
| MLA | 吴新辉 et al. "利用基于残差多注意力和ACON激活函数的神经网络提取建筑物" . | 地球信息科学学报 24 . 04 (2022) : 792-801 . |
| APA | 吴新辉 , 毛政元 , 翁谦 , 施文灶 . 利用基于残差多注意力和ACON激活函数的神经网络提取建筑物 . | 地球信息科学学报 , 2022 , 24 (04) , 792-801 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Semantic segmentation in high-resolution aerial images is a fundamental and challenging task with a wide range of applications. Although many segmentation methods with convolutional neural networks have achieved inspiring results, it is still difficult to distinguish regions with similar spectral features only using high-resolution data. Besides, the traditional data-independent upsampling methods may lead to suboptimal results. This letter proposes a multisensor data fusion model (MSDFM). Following the classical encoder-decoder structure, MSDFM regards colored digital surface models (colored-DSMs) data as a complementary input for further detailed feature extraction. A data-dependent upsampling (DUpsampling) method is adopted in the decoder stage instead of the common upsampling approaches to improve the classification accuracy of pixels of the small objects. Extensive experiments on Vaihingen and Potsdam datasets demonstrate that our proposed MSDFM outperforms most related models. Significantly, segmentation performance for the car category surpasses state-of-the-art methods over the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen dataset.
Keyword ：
Automobiles Automobiles Decoding Decoding Deconvolution Deconvolution Digital surface model (DSM) Digital surface model (DSM) Feature extraction Feature extraction high-resolution aerial images high-resolution aerial images Image segmentation Image segmentation Semantics Semantics semantic segmentation semantic segmentation Vegetation Vegetation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Weng, Qian , Chen, Hao , Chen, Hongli et al. A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Weng, Qian et al. "A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Weng, Qian , Chen, Hao , Chen, Hongli , Guo, Wenzhong , Mao, Zhengyuan . A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
针对最小二乘回归子空间聚类法没有考虑近邻样本对求解表示系数的影响这一不足，提出近邻系数协同强化子空间聚类法.该方法利用近邻样本相似导致表示系数接近的思想定义近邻系数协同强化项.通过近邻样本的系数强化表示系数，从而得到更能反映样本相似度的相似矩阵，进而提高聚类准确率.在6个人脸图像数据集上的实验表明，该方法是有效的.
Keyword ：
人脸图像 人脸图像 协同强化 协同强化 子空间聚类 子空间聚类 近邻系数 近邻系数
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 许毅强 , 夏靖波 , 简彩仁 et al. 近邻系数协同强化人脸图像子空间聚类法 [J]. | 福州大学学报(自然科学版) , 2022 , 50 (05) : 581-586 . |
| MLA | 许毅强 et al. "近邻系数协同强化人脸图像子空间聚类法" . | 福州大学学报(自然科学版) 50 . 05 (2022) : 581-586 . |
| APA | 许毅强 , 夏靖波 , 简彩仁 , 翁谦 . 近邻系数协同强化人脸图像子空间聚类法 . | 福州大学学报(自然科学版) , 2022 , 50 (05) , 581-586 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
