IEEE Journal of Selected Topics in Signal Processing最新文献_第3页

OmniMamba: Omnidirectional Scanning Meets State Space Models for Efficient Hyperspectral Image Classification OmniMamba：全向扫描满足状态空间模型的高效高光谱图像分类

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1109/JSTSP.2026.3657243

Qiyun Zheng;Taosheng Xu;Chenglong Zhang;Peng Li;Wenwen Min;Changmiao Wang

{"title":"OmniMamba: Omnidirectional Scanning Meets State Space Models for Efficient Hyperspectral Image Classification","authors":"Qiyun Zheng;Taosheng Xu;Chenglong Zhang;Peng Li;Wenwen Min;Changmiao Wang","doi":"10.1109/JSTSP.2026.3657243","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3657243","url":null,"abstract":"Accurate classification of hyperspectral images (HSI) is crucial for earth observation and agricultural production analysis, yet remains challenging due to high dimensionality, spectral variability, and limited training samples. Traditional approaches often struggle to effectively balance computational complexity with the ability to capture spatial-spectral relationships of observation targets. To address this challenge, we propose OmniMamba, a novel omnidirectional state space model that adopts a collaborative alternation strategy integrating single-scale and multi-scale feature processing with omnidirectional scanning mechanisms. Four complementary scanning patterns (row, column, zigzag, and snake) are employed in the omnidirectional scanning mechanism to transfer 2D spatial data into 1D spatially structured feature sequence, which preserves directional sensitivity while achieving global dependency modeling with only linear complexity. This avoids the quadratic complexity bottleneck inherent in self-attention mechanisms. Our collaborative alternation strategy coordinates fine-grained spectral signatures with hierarchical spatial contexts through cascaded processing stages, addressing the spectral-spatial feature fusion challenge in HSI classification. Extensive experiments conducted on four benchmark datasets validate the superiority of OmniMamba, achieving a mean overall accuracy of 99.28%, significantly outperforming existing methods. Remarkably, our model accomplishes the performance with only 246 K parameters and 0.04 GFLOPs, demonstrating dramatically low computational complexity than the state-of-the art conventional CNN and transformer-based architectures.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"142-152"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation StyleAvatar3D：利用图像-文本扩散模型高保真3D头像生成

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1109/JSTSP.2026.3662496

Chi Zhang;Yiwen Chen;Yijun Fu;Wei Cheng;Zhenglin Zhou;Wenjia Jiang;Zhibin Wang;Bin Fu;Tao Chen;Gang Yu;Guosheng Lin;Chenxi Song

{"title":"StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation","authors":"Chi Zhang;Yiwen Chen;Yijun Fu;Wei Cheng;Zhenglin Zhou;Wenjia Jiang;Zhibin Wang;Bin Fu;Tao Chen;Gang Yu;Guosheng Lin;Chenxi Song","doi":"10.1109/JSTSP.2026.3662496","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3662496","url":null,"abstract":"The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avatars that utilizes pre-trained image-text diffusion models for data generation and a Generative Adversarial Network (GAN)-based 3D generation network for training. Our method leverages the comprehensive priors of appearance and geometry offered by image-text diffusion models to generate multi-view images of avatars in various styles. During data generation, we employ poses extracted from existing 3D models to guide the generation of multi-view images. To handle inaccurate pose annotations of stylized images, we investigate view-specific prompts and develop a coarse-to-fine discriminator for GAN training. Additionally, we develop a latent diffusion model within the style space of StyleGAN to enable the generation of avatars based on image or text inputs. Our approach demonstrates superior performance over current state-of-the-art methods in terms of visual quality and diversity of the produced avatars.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"192-201"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data 使用深度标记数据缩放作物分割基础模型

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1109/JSTSP.2026.3654362

Jiafei Zhang;Songliang Cao;Binghui Xu;Yanan Li;Weiwei Jia;Tingting Wu;Hao Lu;Weijuan Hu;Zhiguo Han

{"title":"DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data","authors":"Jiafei Zhang;Songliang Cao;Binghui Xu;Yanan Li;Weiwei Jia;Tingting Wu;Hao Lu;Weijuan Hu;Zhiguo Han","doi":"10.1109/JSTSP.2026.3654362","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3654362","url":null,"abstract":"We introduce DepthCropSeg++, a foundation model for crop segmentation, capable of segmenting different crop species under open in-field environment. Crop segmentation is a fundamental task for modern agriculture, underpinning many downstream tasks such as plant phenotyping, density estimation, and weed control. In foundation-model era, a number of generic large language and vision models have been developed. These models have demonstrated remarkable real-world generalization due to significant model capacity and large-scale datasets. However, current crop segmentation models mostly learn from limited data due to expensive pixel-level labelling cost, often performing well only under specific crop types or controlled environment. In this work, we follow the vein of our previous work DepthCropSeg, an almost unsupervised approach to crop segmentation, to scale up a cross-species and cross-scene crop segmentation dataset, with 28,406 images across 30+ species and 15 environmental conditions. We build upon a state-of-the-art semantic segmentation architecture ViT-Adapter, enhance it with dynamic upsampling for improved. detail awareness, and train it with a two-stage self-training pipeline. To systematically validate model performance, we conduct comprehensive experiments to justify the effectiveness and generalization capabilities across multiple crop datasets. Results demonstrate that DepthCropSeg++ achieves 93.11% mIoU on a comprehensive testing set, outperforming both supervised baselines and general-purpose vision foundation models like Segmentation Anything Model (SAM) by significant margins (<inline-formula><tex-math>$+0.36%$</tex-math></inline-formula> and +48.57% respectively). The model particularly excels in challenging scenarios including night-time environment (86.90% mIoU), high-density canopies (99.86% mIoU), and unseen crop varieties (90.09% mIoU), indicating a new state of the art for crop segmentation.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"129-141"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multidimensional Tactile Feature Information Fusion Method for Food Graininess Evaluation 食品粒度评价的多维触觉特征信息融合方法

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1109/JSTSP.2026.3665106

Liwen Huang;Feng Gao;Gaofeng Li;Jun Wang;Dongdong Du

{"title":"A Multidimensional Tactile Feature Information Fusion Method for Food Graininess Evaluation","authors":"Liwen Huang;Feng Gao;Gaofeng Li;Jun Wang;Dongdong Du","doi":"10.1109/JSTSP.2026.3665106","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3665106","url":null,"abstract":"Conventional texture instruments rely on limited feature parameters, which poses challenges for the accurate quantitative evaluation of food graininess. In this study, a multidimensional tactile feature fusion method was proposed to assess food graininess, by using a self-developed tactile information acquisition system. A total of 180 sets of tactile friction and vibration data were collected based on six types of food-saliva mixture samples. Four machine learning algorithms were applied for feature extraction, and the convolutional neural network (CNN) exhibited the best performance, enabling the identification of nine key tactile friction and vibration features. These features, together with sensory evaluation scores, were utilized for correlation analysis, qualitative discrimination, and quantitative prediction. The results demonstrated that tactile vibration features exhibited a stronger correlation with sensory graininess than friction features. Principal component analysis (PCA) effectively discriminated food samples with different graininess levels, achieving an accuracy exceeding 95.8%. Furthermore, graininess was accurately predicted by a stepwise multiple linear regression (SMLR) model (<inline-formula><tex-math>$R_{p} = 0.889$</tex-math></inline-formula>, RMSEP = 0.622). Overall, these findings confirm the effectiveness of multidimensional tactile feature information fusion methods for evaluating food graininess and highlight their potential for the quantitative characterization of food texture.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"168-176"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-30 DOI: 10.1109/JSTSP.2026.3675759

引用次数: 0

IEEE Signal Processing Society Publication Information IEEE信号处理学会出版物信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-30 DOI: 10.1109/JSTSP.2026.3675763

引用次数: 0

Dual-Interaction Spatiotemporal Fusion Network for Agricultural Remote Sensing Imagery 农业遥感影像双交互时空融合网络

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2025-11-10 DOI: 10.1109/JSTSP.2025.3631404

Minghua Wang;Bo Shang;Jing Yao;Rongqiang Zhao;Xin Zhao

{"title":"Dual-Interaction Spatiotemporal Fusion Network for Agricultural Remote Sensing Imagery","authors":"Minghua Wang;Bo Shang;Jing Yao;Rongqiang Zhao;Xin Zhao","doi":"10.1109/JSTSP.2025.3631404","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3631404","url":null,"abstract":"Spatiotemporal fusion (STF) enables the integration of remote sensing (RS) images with high spatial and temporal resolutions, playing a vital role in agricultural applications such as crop monitoring and disease/pest early warning. Recently, Transformer has been applied in the field of STF and has aroused widespread attention due to its powerful representation ability for capturing global long-term dependence features. However, the Transformer applied to STF utilizes self-attention with spatial windows, coupled with channel-wise weight sharing, which significantly constrains cross-spectral adaptive modeling. In addition, conventional feature-extraction frameworks exhibit substantial attenuation of high-frequency components, resulting in spatially blurred fusion outputs. In this study, we propose a Dual-Interaction Network (DINet) for STF of agricultural RS. The DINet operates on two cross-paired inputs (a fine reference image and a coarse target image), significantly relaxing the strict requirements of conventional STF methods. In the proposed framework, a spatial-channel Transformer (SCT) is developed to adjust receptive fields in light of the distinct resolutions of coarse/fine inputs, enhancing spatial-channel feature interaction during the feature-extraction stage. A wavelet decomposition-enhanced strategy is leveraged to integrate high-frequency details and low-frequency global information through a spatial-temporal Transformer (STT). In the reconstruction stage, a multi-level fusion module (MLFM) is designed to hierarchically fuse the features from multiple branches, boosting the spatial-temporal consistency. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in preserving fine-grained agricultural features while maintaining temporal consistency, achieving superior performance for crop monitoring tasks.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"100-114"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Research on Feeding Rate Measurement Method for Combine Harvester Based on Multimodal Information Fusion 基于多模态信息融合的联合收割机进料速率测量方法研究

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1109/JSTSP.2025.3636724

Qian Wang;Yanxin Yin;Zhijun Meng;Chunjiang Zhao;Xiaofei An;Junjie Zhao;Wuchang Qin;Duanyang Geng

{"title":"Research on Feeding Rate Measurement Method for Combine Harvester Based on Multimodal Information Fusion","authors":"Qian Wang;Yanxin Yin;Zhijun Meng;Chunjiang Zhao;Xiaofei An;Junjie Zhao;Wuchang Qin;Duanyang Geng","doi":"10.1109/JSTSP.2025.3636724","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3636724","url":null,"abstract":"The feeding rate is a critical parameter for efficient and low-loss operation of grain combine harvesters. Current measurement technologies face challenges such as low accuracy, poor stability, and delayed results. To address these issues, this study proposes a feeding rate measurement method based on multimodal information fusion, integrating crop visual perception and harvester cutting load. An image sensor captures crop images in front of the harvester, and an improved Deeplab image segmentation algorithm is used to measure crop height. Real-time cutter load data calculates the cutting area, and combining this with the harvester's operational speed enables precise feeding rate calculation, effectively addressing interference caused by complex working conditions. To reduce the impact of vibrations and electromagnetic environments on sensor signals, an M-E fusion filter is designed to extract effective cutting resistance information, ensuring stable and accurate calculations. Using ROS communication and Python 3.6, an intelligent feeding rate measurement system was developed, supporting sensor data acquisition, multi-sensor data fusion computation, and human-computer interaction. Field tests showed that at operating speeds of 2–5 km/h, the average relative error of the feeding rate measurement is 1.64%, with a root mean square error of 0.103×10−3 m3·s−1. Under crop densities of 138–553 plants/m2, the average relative error is 1.89%, with a root mean square error of 0.061×10−3 m3 s−1. The system demonstrates high accuracy and stability, providing technical support for intelligent combine harvester control and offering significant practical value in agricultural machinery research.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"115-128"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Signal Processing Society Publication Information IEEE信号处理学会出版物信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-02-24 DOI: 10.1109/JSTSP.2026.3661776

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IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-02-24 DOI: 10.1109/JSTSP.2026.3661774

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