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Journal articles on the topic 'Spectral-semantic model'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Liu, Dongxu, Qingqing Li, Meihui Li, and Jianlin Zhang. "A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification." Remote Sensing 15, no. 18 (2023): 4642. http://dx.doi.org/10.3390/rs15184642.

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Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic

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Chen, Yuhan, Qingyun Yan, and Weimin Huang. "MSSFF: Advancing Hyperspectral Classification through Higher-Accuracy Multistage Spectral–Spatial Feature Fusion." Remote Sensing 15, no. 24 (2023): 5717. http://dx.doi.org/10.3390/rs15245717.

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This paper presents the MSSFF (multistage spectral–spatial feature fusion) framework, which introduces a novel approach for semantic segmentation from hyperspectral imagery (HSI). The framework aims to simplify the modeling of spectral relationships in HSI sequences and unify the architecture for semantic segmentation of HSIs. It incorporates a spectral–spatial feature fusion module and a multi-attention mechanism to efficiently extract hyperspectral features. The MSSFF framework reevaluates the potential impact of spectral and spatial features on segmentation models and leverages the spectral

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Shen, Feiyu. "Research on the Application of Computer Big Data Technology in English Online Translation." Highlights in Science, Engineering and Technology 68 (October 9, 2023): 351–56. http://dx.doi.org/10.54097/hset.v68i.12499.

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Aiming at the disadvantages of the traditional rule-based machine translation model that the English translation results are not accurate enough and it is difficult to accurately describe the relationship between words, the English machine translation model based on the semantic network is designed and improved. The algorithm analyses the English grammatical rules, then performs Gaussian marginalization on the semantics to obtain the rectangular window function, obtains the window feature vector, projects the semantic information entropy data, and adds the semantic correlation factors to the i

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Zhu, Qiqi, Yanfei Zhong, and Liangpei Zhang. "SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 451–57. http://dx.doi.org/10.5194/isprs-archives-xli-b7-451-2016.

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Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene clas

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Zhu, Qiqi, Yanfei Zhong, and Liangpei Zhang. "SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 451–57. http://dx.doi.org/10.5194/isprsarchives-xli-b7-451-2016.

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Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene clas

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Guo, Yu Tang, and Chang Gang Han. "Automatic Image Annotation Using Semantic Subspace Graph Spectral Clustering Algorithm." Advanced Materials Research 271-273 (July 2011): 1090–95. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1090.

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Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic simila

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Pi, Weiqiang, Tao Zhang, Rongyang Wang, Guowei Ma, Yong Wang, and Jianmin Du. "Semantic-Guided Transformer Network for Crop Classification in Hyperspectral Images." Journal of Imaging 11, no. 2 (2025): 37. https://doi.org/10.3390/jimaging11020037.

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The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitations when processing hyperspectral remote sensing images, primarily in the following aspects. First, the complex background in the images. Various elements in the background may have similar spectral characteristics to the crops, and this spectral similarity makes the classification model susceptible to background interference, thus reducing classi

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Wu, Hao, Canhai Li, and Yongchang Li. "Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 267–74. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-267-2024.

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Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification.

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Du, Zhen, Senhao Liu, Yao Liao, et al. "UniHSFormer X for Hyperspectral Crop Classification with Prototype-Routed Semantic Structuring." Agriculture 15, no. 13 (2025): 1427. https://doi.org/10.3390/agriculture15131427.

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Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling

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Liu, Haijun, Fenglei Chen, Zhihong Zeng, and Xiaoheng Tan. "AMFuse: Add–Multiply-Based Cross-Modal Fusion Network for Multi-Spectral Semantic Segmentation." Remote Sensing 14, no. 14 (2022): 3368. http://dx.doi.org/10.3390/rs14143368.

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Multi-spectral semantic segmentation has shown great advantages under poor illumination conditions, especially for remote scene understanding of autonomous vehicles, since the thermal image can provide complementary information for RGB image. However, methods to fuse the information from RGB image and thermal image are still under-explored. In this paper, we propose a simple but effective module, add–multiply fusion (AMFuse) for RGB and thermal information fusion, consisting of two simple math operations—addition and multiplication. The addition operation focuses on extracting cross-modal comp

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Ashok Kumar, L., M. R. Ebenezar Jebarani, and V. Gokula Krishnan. "Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (2023): 86–93. http://dx.doi.org/10.17762/ijritcc.v11i2.6132.

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Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may b

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Liu, Baisen, Zongting Jia, Penggang Guo, and Weili Kong. "Hyperspectral Image Classification Based on Transposed Convolutional Neural Network Transformer." Electronics 12, no. 18 (2023): 3879. http://dx.doi.org/10.3390/electronics12183879.

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Hyperspectral imaging is a technique that captures images of objects within a wide spectrum range, allowing for the acquisition of additional spectral information to reveal subtle variations and compositional components in the objects. Convolutional neural networks (CNNs) have shown remarkable feature extraction capabilities for HSI classification, but their ability to capture deep semantic features is limited. On the other hand, transformer models based on attention mechanisms excel at handling sequential data and have demonstrated great potential in various applications. Motivated by these t

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Wang, Yi, Wenke Yu, and Zhice Fang. "Multiple Kernel-Based SVM Classification of Hyperspectral Images by Combining Spectral, Spatial, and Semantic Information." Remote Sensing 12, no. 1 (2020): 120. http://dx.doi.org/10.3390/rs12010120.

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In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended morphological profile, Gabor features, and superpixel-based segmentation map. To model spatial information, the extended morphological profile and Gabor features are used to represent structure and texture features, respectively. Moreover, the mean filtering is performed within each superpixel to maint

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Yang, J., and Z. Kang. "INDOOR SEMANTIC SEGMENTATION FROM RGB-D IMAGES BY INTEGRATING FULLY CONVOLUTIONAL NETWORK WITH HIGHER-ORDER MARKOV RANDOM FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 717–24. http://dx.doi.org/10.5194/isprs-archives-xlii-4-717-2018.

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<p><strong>Abstract.</strong> Indoor scenes have the characteristics of abundant semantic categories, illumination changes, occlusions and overlaps among objects, which poses great challenges for indoor semantic segmentation. Therefore, we in this paper develop a method based on higher-order Markov random field model for indoor semantic segmentation from RGB-D images. Instead of directly using RGB-D images, we first train and perform RefineNet model only using RGB information for generating the high-level semantic information. Then, the spatial location relationship from dept

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Wu, Chunxiao, Wei Jia, Jianyu Yang, Tingting Zhang, Anjin Dai, and Han Zhou. "Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery." Remote Sensing 15, no. 10 (2023): 2500. http://dx.doi.org/10.3390/rs15102500.

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Economic fruit forest is an important part of Chinese agriculture with high economic value and ecological benefits. Using UAV multi-spectral images to research the classification of economic fruit forests based on deep learning is of great significance for accurately understanding the distribution and scale of fruit forests and the status quo of national economic fruit forest resources. Based on the multi-spectral remote sensing images of UAV, this paper constructed semantic segmentation data of economic fruit forests, conducted a comparative study on the classification and identification of e

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Wang, Minhui, Yaxiu Sun, Jianhong Xiang, Rui Sun, and Yu Zhong. "Adaptive Learnable Spectral–Spatial Fusion Transformer for Hyperspectral Image Classification." Remote Sensing 16, no. 11 (2024): 1912. http://dx.doi.org/10.3390/rs16111912.

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In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a n

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Akcay, Ozgun, Ahmet Cumhur Kinaci, Emin Ozgur Avsar, and Umut Aydar. "Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+." ISPRS International Journal of Geo-Information 11, no. 1 (2021): 23. http://dx.doi.org/10.3390/ijgi11010023.

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In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that

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Zhang, Zhisheng, Jinsong Tang, Heping Zhong, Haoran Wu, Peng Zhang, and Mingqiang Ning. "Spectral Normalized CycleGAN with Application in Semisupervised Semantic Segmentation of Sonar Images." Computational Intelligence and Neuroscience 2022 (April 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/1274260.

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The effectiveness of CycleGAN is demonstrated to outperform recent approaches for semisupervised semantic segmentation on public segmentation benchmarks. In contrast to analog images, however, the acoustic images are unbalanced and often exhibit speckle noise. As a consequence, CycleGAN is prone to mode-collapse and cannot retain target details when applied directly to the sonar image dataset. To address this problem, a spectral normalized CycleGAN network is presented, which applies spectral normalization to both generators and discriminators to stabilize the training of GANs. Without using a

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Tao, Jie, Yaocai Wu, Xiaolong Zhou, Qike Shao, and Sixian Chan. "A Fusion Model for Saliency Detection Based on Semantic Soft Segmentation." Electronics 11, no. 17 (2022): 2712. http://dx.doi.org/10.3390/electronics11172712.

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With the rapid development of neural networks in recent years, saliency detection based on deep learning has made great breakthroughs. Most deep saliency detection algorithms are based on convolutional neural networks, which still have great room for improvement in the edge accuracy of salient objects recognition, which may lead to fuzzy results in practical applications such as image matting. In order to improve the accuracy of detection, a saliency detection model based on semantic soft segmentation is proposed in this paper. Firstly, the semantic segmentation module combines spectral extinc

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Zhong, Jianyi, Tao Zeng, Zhennan Xu, et al. "A Frequency Attention-Enhanced Network for Semantic Segmentation of High-Resolution Remote Sensing Images." Remote Sensing 17, no. 3 (2025): 402. https://doi.org/10.3390/rs17030402.

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Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics of these images. Traditional methods often prioritize spatial information while underutilizing the rich spectral context, leading to limited feature discrimination capabilities. To address these issues, we propose a novel frequency attention-enhanced network (FAENet), which incorporates a frequency attention model (FreqA) to jointly model spectral and spatial contexts. FreqA leverages discrete wavelet transformation (DWT) to decompose inpu

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Tang, Xiaojiang, Baoxia Li, Junwei Guo, Wenzhuo Chen, Dan Zhang, and Feng Huang. "A Cross-Modal Feature Fusion Model Based on ConvNeXt for RGB-D Semantic Segmentation." Mathematics 11, no. 8 (2023): 1828. http://dx.doi.org/10.3390/math11081828.

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Semantic segmentation, as the pixel level classification with dividing an image into multiple blocks based on the similarities and differences of categories (i.e., assigning each pixel in the image to a class label), is an important task in computer vision. Combining RGB and Depth information can improve the performance of semantic segmentation. However, there is still a problem of the way to deeply integrate RGB and Depth. In this paper, we propose a cross-modal feature fusion RGB-D semantic segmentation model based on ConvNeXt, which uses ConvNeXt as the skeleton network and embeds a cross-m

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Xu, Qinfu, Shaozu Yuan, Yiwei Wei, Jie Wu, Leiquan Wang, and Chunlei Wu. "Multiple Feature Refining Network for Visual Emotion Distribution Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 8924–32. https://doi.org/10.1609/aaai.v39i9.32965.

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The significance of visual emotion distribution learning (VEDL) has surged, particularly with the growing inclination to convey emotions through images. The key of VEDL lies in capturing both low- and high-level features within the same visual content, thus promoting the model for salient and subtle emotion awareness. To learn the distribution of emotions involved in images, most previous works learn coarse semantic knowledge with unbiased filtering. Consequently, they focus on the entire scene and suffer from the redundancy of semantic-irrelevant information, which diminishes the affective co

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Cheng, Xu, Lihua Liu, and Chen Song. "A Cyclic Information–Interaction Model for Remote Sensing Image Segmentation." Remote Sensing 13, no. 19 (2021): 3871. http://dx.doi.org/10.3390/rs13193871.

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Object detection and segmentation have recently shown encouraging results toward image analysis and interpretation due to their promising applications in remote sensing image fusion field. Although numerous methods have been proposed, implementing effective and efficient object detection is still very challenging for now, especially for the limitation of single modal data. The use of a single modal data is not always enough to reach proper spectral and spatial resolutions. The rapid expansion in the number and the availability of multi-source data causes new challenges for their effective and

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Song, Hong, Syed Raza Mehdi, Yangfan Zhang, et al. "Development of Coral Investigation System Based on Semantic Segmentation of Single-Channel Images." Sensors 21, no. 5 (2021): 1848. http://dx.doi.org/10.3390/s21051848.

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Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of underwater life. However, a severe decline in the coral resources can be noted in the last decades due to global environmental changes causing marine pollution. Hence, it is of paramount importance to develop and deploy swift coral monitoring system to alleviate the destruction of corals. Performing semantic segmentation on underwater images is one of the most efficient methods for automatic investigation of corals. Firstly, to design a coral investigation system, RGB and spectral images of various types

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Yuan, Qinglie. "Urban Land-use Features Mapping from LiDAR and Remote Sensing Images using Visual Transformer Network Model." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-5-2024 (March 12, 2025): 195–200. https://doi.org/10.5194/isprs-archives-xlviii-m-5-2024-195-2025.

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Abstract. With the rapid development of science and technology in the acceleration of urbanization, it is important to achieve efficient and accurate monitoring and mapping of urban features. Traditional urban feature mapping methods often rely on a single data source, such as optical remote sensing images or LiDAR, which often encounter many challenges in complex urban environments, such as shading, occlusion, and land cover changes. LiDAR has relatively accurate three-dimensional spatial information, while remote sensing image has rich spectral information. Thus, the fusion of spatial-spectr

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Chen, Guanzhou, Xiaoliang Tan, Beibei Guo, et al. "SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation." Remote Sensing 13, no. 23 (2021): 4902. http://dx.doi.org/10.3390/rs13234902.

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Semantic segmentation is a fundamental task in remote sensing image analysis (RSIA). Fully convolutional networks (FCNs) have achieved state-of-the-art performance in the task of semantic segmentation of natural scene images. However, due to distinctive differences between natural scene images and remotely-sensed (RS) images, FCN-based semantic segmentation methods from the field of computer vision cannot achieve promising performances on RS images without modifications. In previous work, we proposed an RS image semantic segmentation framework SDFCNv1, combined with a majority voting postproce

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Koh, Hyeong Il, Sungdae Na, and Myoung Nam Kim. "Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network." Bioengineering 10, no. 11 (2023): 1325. http://dx.doi.org/10.3390/bioengineering10111325.

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Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time–frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time–frequency

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Zhyrnov, V., and S. Solonska. "Intelligent model of radar object images for surveillance radars." Radiotekhnika, no. 212 (March 28, 2023): 148–54. http://dx.doi.org/10.30837/rt.2023.1.212.14.

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The results of developing an intelligent model of radar object images for surveillance radars are presented. The relevance of this work deals with the development of algorithm for automatic processing images of radar objects that provide effective detection of weak true signals due to the accumulation of signal and logical information in the analyzed cell and in its surroundings under interferences. The improvement of air safety tools and the automation of air traffic management processes require effective procedures to process signal information. The issues of more complete use and qualitativ

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Wang, Jinxin, Manman Wang, Kaiwei Cong, and Zilong Qin. "A Semantic Segmentation Method for Remote Sensing Images Based on an Improved TransDeepLab Model." Land 14, no. 1 (2024): 22. https://doi.org/10.3390/land14010022.

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Due to the various types of land cover and large spectral differences in remote sensing images, high-quality semantic segmentation of these images still faces challenges such as fuzzy object boundary extraction and difficulty in identifying small targets. To address these challenges, this study proposes a new improved model based on the TransDeepLab segmentation method. The model introduces a GAM attention mechanism in the coding stage, and incorporates a multi-level linear up-sampling strategy in the decoding stage. These enhancements allow the model to fully utilize multi-level semantic info

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Qin, Jinchun, and Hongrui Zhao. "Spatial-Spectral-Associative Contrastive Learning for Satellite Hyperspectral Image Classification with Transformers." Remote Sensing 15, no. 6 (2023): 1612. http://dx.doi.org/10.3390/rs15061612.

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Albeit hyperspectral image (HSI) classification methods based on deep learning have presented high accuracy in supervised classification, these traditional methods required quite a few labeled samples for parameter optimization. When processing HSIs, however, artificially labeled samples are always insufficient, and class imbalance in limited samples is inevitable. This study proposed a Transformer-based framework of spatial–spectral–associative contrastive learning classification methods to extract both spatial and spectral features of HSIs by the self-supervised method. Firstly, the label in

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Zhang, Meng, Yi Yang, Sixian Zhang, Pengbo Mi, and Deqiang Han. "Spectral-Spatial Center-Aware Bottleneck Transformer for Hyperspectral Image Classification." Remote Sensing 16, no. 12 (2024): 2152. http://dx.doi.org/10.3390/rs16122152.

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Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral variability and the limited labeled samples create challenges for HSI classification, which results in poor data separability and makes it difficult to learn highly discriminative semantic features. In order to address the above problems, a novel spectral-spatial center-aware bottleneck Transformer is proposed. First, the highly relevant spectral i

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Li, Kang, and Yizhang Jiang. "Prediction Method of Biological Fermentation Data Based on Deep Neural Network." Journal of Physics: Conference Series 2278, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2278/1/012029.

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Abstract This paper proposes a (Takagi-Sugeno-Kang) TSK fuzzy regression model that based on self-supervised learning and deep autoencoder to predict and monitor the real-time concentration of each ingredient in the fermentation process. The entire model consists of the following steps: obtaining and preprocessing sample spectral data to obtain a training set; using the training set to train a self-supervised feature extraction network model to optimize the parameters of the feature extraction network model; training the autoencoder network model to establish a dimensionality reduction model b

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Yan, L., and W. Xia. "A MODIFIED THREE-DIMENSIONAL GRAY-LEVEL CO-OCCURRENCE MATRIX FOR IMAGE CLASSIFICATION WITH DIGITAL SURFACE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 133–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-133-2019.

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<p><strong>Abstract.</strong> 2D texture cannot reflect the 3D object’s texture because it only considers the intensity distribution in the 2D image region but int real world the intensities of objects are distributed in 3D surface. This paper proposes a modified three-dimensional gray-level co-occurrence matrix (3D-GLCM) which is first introduced to process volumetric data but cannot be used directly to spectral images with digital surface model because of the data sparsity of the direction perpendicular to the image plane. Spectral and geometric features combined with no te

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Wen, Teng, Heng Wang, and Liguo Wang. "Dual-Branch Spatial–Spectral Transformer with Similarity Propagation for Hyperspectral Image Classification." Remote Sensing 17, no. 14 (2025): 2386. https://doi.org/10.3390/rs17142386.

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In recent years, Vision Transformers (ViTs) have gained significant traction in the field of hyperspectral image classification due to their advantages in modeling long-range dependency relationships between spectral bands and spatial pixels. However, after stacking multiple Transformer encoders, challenges pertaining to information degradation may emerge during the forward propagation. That is to say, existing Transformer-based methods exhibit certain limitations in retaining and effectively utilizing information throughout their forward transmission. To tackle these challenges, this paper pr

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Yin, Junru, Xuan Liu, Ruixia Hou, et al. "Multiscale Pixel-Level and Superpixel-Level Method for Hyperspectral Image Classification: Adaptive Attention and Parallel Multi-Hop Graph Convolution." Remote Sensing 15, no. 17 (2023): 4235. http://dx.doi.org/10.3390/rs15174235.

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Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have led to promising advancements in hyperspectral image (HSI) classification; however, traditional CNNs with fixed square convolution kernels are insufficiently flexible to handle irregular structures. Similarly, GCNs that employ superpixel nodes instead of pixel nodes may overlook pixel-level features; both networks tend to extract features locally and cause loss of multilayer contextual semantic information during feature extraction due to the fixed kernel. To leverage the strengths of CNNs and GCNs, we propose a

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Zhang, Jinglin, Yuxia Li, Bowei Zhang, et al. "CD-MQANet: Enhancing Multi-Objective Semantic Segmentation of Remote Sensing Images through Channel Creation and Dual-Path Encoding." Remote Sensing 15, no. 18 (2023): 4520. http://dx.doi.org/10.3390/rs15184520.

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As a crucial computer vision task, multi-objective semantic segmentation has attracted widespread attention and research in the field of remote sensing image analysis. This technology has important application value in fields such as land resource surveys, global change monitoring, urban planning, and environmental monitoring. However, multi-target semantic segmentation of remote sensing images faces challenges such as complex surface features, complex spectral features, and a wide spatial range, resulting in differences in spatial and spectral dimensions among target features. To fully exploi

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Yuan, Qinglie, and Helmi Zulhaidi Mohd Shafri. "Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction." Remote Sensing 14, no. 19 (2022): 4920. http://dx.doi.org/10.3390/rs14194920.

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Building information extraction utilizing remote sensing technology has vital applications in many domains, such as urban planning, cadastral mapping, geographic information censuses, and land-cover change analysis. In recent years, deep learning algorithms with strong feature construction ability have been widely used in automatic building extraction. However, most methods using semantic segmentation networks cannot obtain object-level building information. Some instance segmentation networks rely on predefined detectors and have weak detection ability for buildings with complex shapes and mu

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Teo, Tee-Ann, and Pei-Cheng Chen. "Building Change Detection in Aerial Imagery Using End-to-End Deep Learning Semantic Segmentation Techniques." Buildings 15, no. 5 (2025): 695. https://doi.org/10.3390/buildings15050695.

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Automatic building change detection is essential for updating geospatial data, urban planning, and land use management. The objective of this study is to propose a transformer-based UNet-like framework for end-to-end building change detection, integrating multi-temporal and multi-source data to improve efficiency and accuracy. Unlike conventional methods that focus on either spectral imagery or digital surface models (DSMs), the proposed method combines RGB color imagery, DSMs, and building vector maps in a three-branch Siamese architecture to enhance spatial, spectral, and elevation-based fea

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Gao, Meixiang, Tingyu Lu, and Lei Wang. "Crop Mapping Based on Sentinel-2 Images Using Semantic Segmentation Model of Attention Mechanism." Sensors 23, no. 15 (2023): 7008. http://dx.doi.org/10.3390/s23157008.

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Using remote sensing images to identify crop plots and estimate crop planting area is an important part of agricultural remote sensing monitoring. High-resolution remote sensing images can provide rich information regarding texture, tone, shape, and spectrum of ground objects. With the advancement of sensor and information technologies, it is now possible to categorize crops with pinpoint accuracy. This study defines crop mapping as a semantic segmentation problem; therefore, a deep learning method is proposed to identify the distribution of corn and soybean using the differences in the spatia

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Tan, Daning, Yu Liu, Gang Li, Libo Yao, Shun Sun, and You He. "Serial GANs: A Feature-Preserving Heterogeneous Remote Sensing Image Transformation Model." Remote Sensing 13, no. 19 (2021): 3968. http://dx.doi.org/10.3390/rs13193968.

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In recent years, the interpretation of SAR images has been significantly improved with the development of deep learning technology, and using conditional generative adversarial nets (CGANs) for SAR-to-optical transformation, also known as image translation, has become popular. Most of the existing image translation methods based on conditional generative adversarial nets are modified based on CycleGAN and pix2pix, focusing on style transformation in practice. In addition, SAR images and optical images are characterized by heterogeneous features and large spectral differences, leading to proble

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Khan, Asim, Warda Asim, Anwaar Ulhaq, and Randall W. Robinson. "A deep semantic vegetation health monitoring platform for citizen science imaging data." PLOS ONE 17, no. 7 (2022): e0270625. http://dx.doi.org/10.1371/journal.pone.0270625.

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Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen scien

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Lin, Lujun, Lei Liu, Ming Liu, et al. "DEDNet: Dual-Encoder DeeplabV3+ Network for Rock Glacier Recognition Based on Multispectral Remote Sensing Image." Remote Sensing 16, no. 14 (2024): 2603. http://dx.doi.org/10.3390/rs16142603.

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Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the cryosphere environment. Deep learning algorithms and red–green–blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for class

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Liang, Xintao, Xinling Li, Qingyan Wang, Jiadong Qian, and Yujing Wang. "Hyperspectral Image Change Detection Method Based on the Balanced Metric." Sensors 25, no. 4 (2025): 1158. https://doi.org/10.3390/s25041158.

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Change detection, as a popular research direction for dynamic monitoring of land cover change, usually uses hyperspectral remote-sensing images as data sources. Hyperspectral images have rich spatial–spectral information, but traditional change detection methods have limited ability to express the features of hyperspectral images, and it is difficult to identify the complex detailed features, semantic features, and spatial–temporal correlation features in two-phase hyperspectral images. Effectively using the abundant spatial and spectral information in hyperspectral images to complete change d

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Zhang, Chengming, Yan Chen, Xiaoxia Yang, et al. "Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion." Remote Sensing 12, no. 2 (2020): 213. http://dx.doi.org/10.3390/rs12020213.

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When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic featu

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Wu, Suichao, Chengjun Chen, and Jinlei Wang. "Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation." Applied Sciences 13, no. 2 (2023): 1182. http://dx.doi.org/10.3390/app13021182.

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Semantic segmentation of assembly images is to recognize the assembled parts and find wrong assembly operations. However, the training of supervised semantic segmentation requires a large amount of labeled data, which is time-consuming and laborious. Moreover, the sizes of mechanical assemblies are not uniform, leading to low segmentation accuracy of small-target objects. This study proposes an adversarial learning network for semi-supervised semantic segmentation of mechanical assembly images (AdvSemiSeg-MA). A fusion method of ASFF multiscale output is proposed, which combines the outputs of

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Mani, Radhakrishna, and Manjunatha Raguttapalli Chowdareddy. "A hybrid spectral-spatial fusion technique for hyperspectral object classification." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 361–69. https://doi.org/10.11591/ijeecs.v33.i1.pp361-369.

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In the field of object classification, hyperspectral imaging (HSI) has been widely used, due to its spectral-spatial, and temporal resolution of larger areas. The HSI is generally used to identify the objects physical properties in accurate manner and as well as to identify similar object with acceptable spectral signatures. Thus, the HSI has been widely used for object identification applications in different fields such as precision agriculture, environmental study, crop monitoring, and surveillance. However, the object classification is time consuming due to extremely large size; thus, the

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Wang, Baoguo, and Yonghui Yao. "Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model." Remote Sensing 16, no. 2 (2024): 256. http://dx.doi.org/10.3390/rs16020256.

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With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction models for vegetation information in mountainous areas. Because the features of remote sensing images (such as spectral, textural and geometric features) change with changes in illumination, viewing angle, scale and spectrum, it is difficult for a remote sensing intelligent interpretation model with a single data source as input to meet the requiremen

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He, L., Z. Wu, Y. Zhang, and Z. Hu. "SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 75–81. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-75-2020.

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Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is

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Wang, Siman, and Qian Zhou. "Multi-Source Fusion Enhanced Feature Segmentation in Remote Sensing Imagery." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 395–401. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-395-2024.

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Abstract. With deepening application of deep learning technology in the field of remote sensing, several challenges persist in the segmentation of remote sensing optical images. These challenges include: (1) insufficient availability of deep learning-based remote sensing semantic segmentation datasets; (2) inadequate utilization of multi-source remote sensing data in the field of semantic segmentation; (3) limited sample size for effective model training, as well as the need to enhance both the speed and accuracy of model training. To address these challenges, this study introduces a multi-sou

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Weng, Zhi, Qiyan Li, Zhiqiang Zheng, and Lixin Wang. "SCR-Net: A Dual-Channel Water Body Extraction Model Based on Multi-Spectral Remote Sensing Imagery—A Case Study of Daihai Lake, China." Sensors 25, no. 3 (2025): 763. https://doi.org/10.3390/s25030763.

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Monitoring changes in lake area using remote sensing imagery and artificial intelligence algorithms is essential for assessing regional ecological balance. However, most current semantic segmentation models primarily rely on the visible light spectrum for feature extraction, which fails to fully utilize the multi-spectral characteristics of remote sensing images. Therefore, this leads to issues such as blurred segmentation of lake boundaries in the imagery, the loss of small water body targets, and incorrect classification of water bodies. Additionally, the practical applicability of existing

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