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Gotowe bibliografie tematyczne / Spectral segmentation / Artykuły w czasopismach

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Artykuły w czasopismach na temat „Spectral segmentation”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Shi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (2023): 828. http://dx.doi.org/10.3390/rs15030828.

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Conside

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Mejia, Daniel, Oscar Ruiz-Salguero, and Carlos A. Cadavid. "Spectral-based mesh segmentation." International Journal on Interactive Design and Manufacturing (IJIDeM) 11, no. 3 (2016): 503–14. http://dx.doi.org/10.1007/s12008-016-0300-0.

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Li, Hongyu, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, and I.-Fan Shen. "Kernel Based Spectral Image Segmentation." Conference on Colour in Graphics, Imaging, and Vision 4, no. 1 (2008): 494–98. http://dx.doi.org/10.2352/cgiv.2008.4.1.art00106.

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Priego, Torres Blanca María, and Richard J. Duro. "An approach for the customized high-dimensional segmentation of remote sensing hyperspectral images." Sensors 19, no. 13 (2019): 2887. https://doi.org/10.3390/s19132887.

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The work presents a methodology for the customized segmentation of remote sensing hyperspectral images using a multigradient cellular automaton (MGCA) approach coupled with an evolutionary algorithm (ECAS-II). The study addresses three main challenges in hyperspectral image segmentation: the need for segmentations tailored to user requirements, the scarcity of adequately labeled reference images, and the loss of information that occurs when high-dimensional images are projected into lower-dimensional spaces before segmentation. The proposed methodology allows for the segmentation of multidimen

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Zhang, Jianwei, Yue Shen, Zhaohui Zheng, and Le Sun. "A Robust Image Segmentation Framework Based on Nonlocal Total Variation Spectral Transform." Wireless Communications and Mobile Computing 2022 (February 24, 2022): 1–20. http://dx.doi.org/10.1155/2022/1442745.

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Image segmentation plays an important role in various computer vision tasks. Nevertheless, noise always inevitably appears in images and brings a big challenge to image segmentation. To handle the problem, we study the nonlocal total variation (NLTV) spectral theory and build up an image segmentation framework with NLTV spectral transform to segment images with noise. Firstly, we decompose an image into the NLTV flow in the NLTV spectral transform, with which the max response time of each pixel is calculated. Secondly, a separation surface is constructed with the max response time to distingui

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6

Wang, Ke, Hainan Chen, Ligang Cheng, and Jian Xiao. "Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices." Remote Sensing 14, no. 2 (2022): 326. http://dx.doi.org/10.3390/rs14020326.

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Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on th

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Mohammadi, Sina, and Mohamed Allali. "Advancing Brain Tumor Segmentation with Spectral–Spatial Graph Neural Networks." Applied Sciences 14, no. 8 (2024): 3424. http://dx.doi.org/10.3390/app14083424.

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In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, w

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8

Yuan, Changan, Xiao Qin, Zhengyou Qin, and Ruili Wang. "Image segmentation based on modified superpixel segmentation and spectral clustering." Journal of Engineering 2018, no. 16 (2018): 1704–11. http://dx.doi.org/10.1049/joe.2018.8320.

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Wang, Hao, Tong Lu, Oscar Kin-Chung Au, and Chiew-Lan Tai. "Spectral 3D mesh segmentation with a novel single segmentation field." Graphical Models 76, no. 5 (2014): 440–56. http://dx.doi.org/10.1016/j.gmod.2014.04.009.

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10

Furth, Yoram, and Stanley R. Rotman. "Efficacy of Segmentation for Hyperspectral Target Detection." Sensors 25, no. 1 (2025): 272. https://doi.org/10.3390/s25010272.

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Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation cases, with no clear explanations for these divergent outcomes. This paper elucidates the conditions under which segmentation might improve detector performance. Focusing on a representative algorithm and assuming a target additive model, the study examines

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11

R., Vrinthavani, and M. Kaimal. "Motion Segmentation Algorithm using Spectral Framework." Defence Science Journal 60, no. 1 (2010): 39–47. http://dx.doi.org/10.14429/dsj.60.102.

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12

Caulfield, H. John, and Jian Fu. "Holographic Spectral Image Discrimination and Segmentation." Journal of Holography and Speckle 3, no. 2 (2006): 112–16. http://dx.doi.org/10.1166/jhs.2006.017.

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13

Li, Hongyu, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, and I.-fan Shen. "Kernel-based spectral color image segmentation." Journal of the Optical Society of America A 25, no. 11 (2008): 2805. http://dx.doi.org/10.1364/josaa.25.002805.

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14

Archip, Neculai, Robert Rohling, Peter Cooperberg, and Hamid Tahmasebpour. "Ultrasound image segmentation using spectral clustering." Ultrasound in Medicine & Biology 31, no. 11 (2005): 1485–97. http://dx.doi.org/10.1016/j.ultrasmedbio.2005.07.005.

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15

O'Callaghan, R. J., and D. R. Bull. "Combined morphological-spectral unsupervised image segmentation." IEEE Transactions on Image Processing 14, no. 1 (2005): 49–62. http://dx.doi.org/10.1109/tip.2004.838695.

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16

Zhang, Jing Mao, and Yan Xia Shen. "Spectral segmentation via minimum barrier distance." Multimedia Tools and Applications 76, no. 24 (2017): 25713–29. http://dx.doi.org/10.1007/s11042-017-4473-8.

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17

Zhu, Hongming, Rui Tan, Letong Han, et al. "DSSM: A Deep Neural Network with Spectrum Separable Module for Multi-Spectral Remote Sensing Image Segmentation." Remote Sensing 14, no. 4 (2022): 818. http://dx.doi.org/10.3390/rs14040818.

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Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on computer vision images (CVIs). However, there is still a lack of exploration of spectra correlation in MS RSIs. In this study, a deep neural network with a spectrum separable module (DSSM) is proposed for semantic segmentation, which enables the utilization of MS characteristics of RSIs. The experimen

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18

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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19

Hauta-Kasari, M., J. Parkkinen, T. Jaaskelainen, and R. Lenz. "Multi-spectral Texture Segmentation Based on the Spectral Cooccurrence Matrix." Pattern Analysis & Applications 2, no. 4 (1999): 275–84. http://dx.doi.org/10.1007/s100440050036.

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20

Dai, Bing, Yingjie Peng, Ning Lin, and Peng Wang. "Bearing Fault Diagnosis Based on Prime Mean Spectral Segmentation Kurtogram." Journal of Physics: Conference Series 2419, no. 1 (2023): 012080. http://dx.doi.org/10.1088/1742-6596/2419/1/012080.

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Abstract The spectrum-based fault feature extraction method for industrial equipment can avoid the problems of modal aliasing and end effects caused by mode decomposition in the time domain. This paper proposes a kurtogram constructed based on prime mean spectral segmentation. The preset framework realizes fast spectral segmentation, and the multivariate segmentation mode provides a more reasonable distribution of center frequency and bandwidth. The precise location and diagnosis of faults can be achieved by scanning shocks in each frequency band by spectral negentropy. Simulation signals and

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21

Chendeb El Rai, Marwa, Muna Darweesh, Aicha Beya Far, and Amjad Gawanmeh. "Spectral and Spatial Attention Fusion for Building Segmentation in Remote Sensing Imagery." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 10, 2025): 181–87. https://doi.org/10.5194/isprs-annals-x-g-2025-181-2025.

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Abstract. The building segmentation in very high resolution remote sensing imagery presents challenges due to the need to delineate features accurately in a wide range of urban landscapes. Many existing building segmentation methods struggle in discerning complex structures and providing fine grained generalisation over different geographic regions. Additionally, these methods often require to extensive preprocessing and struggle to combine multispectral data. Addressing the different challenges, we introduce the Multi-Band Spectral-Spatial Fusion Attention Network (MBSSFA-Net), a novel method

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22

Arefi, Farnoosh, Amir M. Mansourian, and Shohreh Kasaei. "Deep spectral improvement for unsupervised image instance segmentation." PLOS ONE 19, no. 10 (2024): e0307432. http://dx.doi.org/10.1371/journal.pone.0307432.

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Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised bac

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23

Li, Chen Ming, Li Qin Zhu, Qiang Wang, Zhen Sun, Feng Chen Huang, and Chen He. "High-Resolution Remote Multi-Spectral Sensing Images Based on Texture Features." Applied Mechanics and Materials 687-691 (November 2014): 3596–99. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3596.

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Aiming at the difficulties in the segmentation for high-resolution remote multispectral sensing images, this paper proposed a segmentation approach for remote sensing images based on texture features. The algorithm implemented precipitation watershed transform respectively on the texture images obtained by the different characteristics of GLCM, and then superimposed the two segmentation results, finally completing the image segmentation by using a novel regional consolidation method that combined the texture features. The experiments were implemented on the high-resolution ALOS and SPOT 5 remo

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24

Volkova, Natalya P., and Viktor N. Krylov. "VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS." Herald of Advanced Information Technology 3, no. 4 (2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.

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The study shows the need for express systems, in which it is necessary to perform the analysis of texture images in various areas of diagnosis, for example, in medical express diagnostics of dermatologic disorders. Since the reliability of decision-making in such systems depends on the quality of image segmentation, which, as a rule, have the nature of spectral-statistical textures, it is advisable to develop methods for segmentation of such images and models for their presentation. A model of spectral-statistical texture is proposed, which takes into account the random nature of changes in th

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25

McGraw, Tim, Jisun Kang, and Donald Herring. "Sparse Non-Negative Matrix Factorization for Mesh Segmentation." International Journal of Image and Graphics 16, no. 01 (2016): 1650004. http://dx.doi.org/10.1142/s0219467816500042.

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In this paper, we present a method for 3D mesh segmentation based on sparse non-negative matrix factorization (NMF). Image analysis techniques based on NMF have been shown to decompose images into semantically meaningful local features. Since the features and coefficients are represented in terms of non-negative values, the features contribute to the resulting images in an intuitive, additive fashion. Like spectral mesh segmentation, our method relies on the construction of an affinity matrix which depends on the geometric properties of the mesh. We show that segmentation based on the NMF is s

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26

Lopez-Fandino, Javier, Torres Blanca María Priego, Heras Dora B., and Francisco Argüello. "GPU Projection of ECAS-II Segmenter for Hyperspectral Images Based on Cellular Automata." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 1 (2017): 20–28. https://doi.org/10.1109/JSTARS.2016.2588530.

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<strong>"The copyright of this publication belongs exclusively to the publisher, access to the full text is not allowed."</strong> Segmentation plays a crucial role in the analysis of multidimensional images, such as those used in remote sensing. Typically, segmentation algorithms for these images start by reducing their dimensionality, which can lead to the loss of potentially important information for the segmentation process. Evolutionary cellular automata segmentation (ECAS-II) offers an alternative by utilizing a cellular automata-based approach that considers all the spectral information

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K., Pavithra. "Analytical Method of Multi-Objective Genetic Algorithm with Multi-Objective Messy Genetic Algorithm in Satellite Image Segmentation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 3, no. 3 (2018): 168–73. https://doi.org/10.5281/zenodo.4301122.

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Image can be dividing into different Segmentation. In image processing , the important task is Segmentation process methods. This method involves such as K-means clustering, watershed segmentation, Fuzzy c-Means, Iterative Self Organizing Data. Clustering methods depends powerfully on the selection of the primary spectral signatures which represents initial cluster centers. Normally, this is either done physically or erratically based on statistical operations. In this case the outcome is random and sometime inaccurate. In base paper an unsupervised method based on Multi-Objective Genetic Algo

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Noyel, Guillaume, Jesús Angulo, and Dominique Jeulin. "MORPHOLOGICAL SEGMENTATION OF HYPERSPECTRAL IMAGES." Image Analysis & Stereology 26, no. 3 (2011): 101. http://dx.doi.org/10.5566/ias.v26.p101-109.

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The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted to the different hyperspectral functions. Data reduction is performed either by Factor Analysis or by model fitting. Image segmentation is done on different spaces: factor space, parameters space, etc. On all these spaces the spatial/spectral se

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Chen, Chao, Yue Jiang, and Xiaoqing Zhu. "Research on Lettuce Canopy Image Processing Method Based on Hyperspectral Imaging Technology." Plants 13, no. 23 (2024): 3403. https://doi.org/10.3390/plants13233403.

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For accurate segmentation of lettuce canopy images, dealing with uneven illumination and background interference, hyperspectral imaging technology was applied to capture images of lettuce from the rosette to nodule stages. The spectral ratio method was used to select the characteristic wavelengths, and the characteristic wavelength images were denoised and image fused before being processed by filtering and threshold segmentation. To verify the accuracy of this segmentation method, the manual segmentation method and the segmentation method used in this study were compared, and the area overlap

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Naveed, Faizaan, Baoxin Hu, Jianguo Wang, and G. Brent Hall. "Individual Tree Crown Delineation Using Multispectral LiDAR Data." Sensors 19, no. 24 (2019): 5421. http://dx.doi.org/10.3390/s19245421.

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In this study, multispectral Light Detection and Ranging (LiDAR) data were utilized to improve delineation of individual tree crowns (ITC) as an important step in individual tree analysis. A framework to integrate spectral and height information for ITC delineation was proposed, and the multi-scale algorithm for treetop detection developed in one of our previous studies was improved. In addition, an advanced region-based segmentation method that used detected treetops as seeds was proposed for segmentation of individual crowns based on their spectral, contextual, and height information. The pr

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31

Lin, Lianlei, and Shanshan Zhang. "Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information." Applied Sciences 10, no. 4 (2020): 1261. http://dx.doi.org/10.3390/app10041261.

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Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy was applied to measure the image information and preliminarily select spectral bands. Mutual information (MI) is derived from the concept of entropy and measures the statistical dependence between two random variables. Also, MI can effectively identify the redundant spectral bands. Theref

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Alkhatib, Mohammed Q., and Miguel Velez-Reyes. "Improved Spatial-Spectral Superpixel Hyperspectral Unmixing." Remote Sensing 11, no. 20 (2019): 2374. http://dx.doi.org/10.3390/rs11202374.

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In this paper, an unsupervised unmixing approach based on superpixel representation combined with regional partitioning is presented. A reduced-size image representation is obtained using superpixel segmentation where each superpixel is represented by its mean spectra. The superpixel image representation is then partitioned into regions using quadtree segmentation based on the Shannon entropy. Spectral endmembers are extracted from each region that corresponds to a leaf of the quadtree and combined using clustering into endmember classes. The proposed approach is tested and validated using the

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Kumar, A., and H. Wakita. "A segmentation algorithm based on spectral variance." Journal of the Acoustical Society of America 83, S1 (1988): S54. http://dx.doi.org/10.1121/1.2025407.

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Tae Hoon Kim, Kyoung Mu Lee, and Sang Uk Lee. "Learning Full Pairwise Affinities for Spectral Segmentation." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 7 (2013): 1690–703. http://dx.doi.org/10.1109/tpami.2012.237.

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Chahhou, Mohamed, Lahcen Moumoun, Mohamed El Far, and Taoufiq Gadi. "Segmentation of 3D Meshes Usingp-Spectral Clustering." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 8 (2014): 1687–93. http://dx.doi.org/10.1109/tpami.2013.2297314.

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Tung, Frederick, Alexander Wong, and David A. Clausi. "Enabling scalable spectral clustering for image segmentation." Pattern Recognition 43, no. 12 (2010): 4069–76. http://dx.doi.org/10.1016/j.patcog.2010.06.015.

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Zhang, Songyang, Shuguang Cui, and Zhi Ding. "Hypergraph Spectral Clustering for Point Cloud Segmentation." IEEE Signal Processing Letters 27 (2020): 1655–59. http://dx.doi.org/10.1109/lsp.2020.3023587.

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Mohd Mokhtar, Nur Salma, Khairuddin Osman, Khairul Muzzammil Saipullah, and Muhammad Haziq Faris Hasnol. "Multispectral image segmentation using localized spectral binarization." IOP Conference Series: Materials Science and Engineering 210 (June 2017): 012044. http://dx.doi.org/10.1088/1757-899x/210/1/012044.

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Qiu, Hongjie, and James M. Keller. "Multiple spectral image segmentation using fuzzy techniques." International Journal of Approximate Reasoning 2, no. 2 (1988): 105. http://dx.doi.org/10.1016/0888-613x(88)90089-8.

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Liu, Xinlin, Viktor Krylov, Su Jun, et al. "Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology." Mathematical Biosciences and Engineering 19, no. 7 (2022): 6923–39. http://dx.doi.org/10.3934/mbe.2022326.

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<abstract> <p>An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved v

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Jiao, Jiao, Lingda Wu, and Kechang Qian. "A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation." Electronics 8, no. 6 (2019): 685. http://dx.doi.org/10.3390/electronics8060685.

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In order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, different connected component groups can be obtained according to their spectral characteristics. For spectral information modulation of fusion images, the LASM coefficients are constructed based on details extracted from images and local spectral relationships among MS bands. Moreover, we introduce a coope

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42

Pang, Haiyang, Aiwu Zhang, Xiaoyan Kang, Nianpeng He, and Gang Dong. "Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images." Remote Sensing 12, no. 24 (2020): 4155. http://dx.doi.org/10.3390/rs12244155.

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An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts

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Zakani, F. R., M. Bouksim, K. Arhid, M. Aboulfatah, and T. Gadi. "Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering." Computer Optics 42, no. 2 (2018): 312–19. http://dx.doi.org/10.18287/2412-6179-2018-42-2-312-319.

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3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use th

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Wang, Caiqiong, Lei Zhao, Wangfei Zhang, Xiyun Mu, and Shitao Li. "Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning." PeerJ 10 (January 19, 2022): e12805. http://dx.doi.org/10.7717/peerj.12805.

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Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get

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Zhang, Yuhan, Xi Wang, Haishu Tan, Chang Xu, Xu Ma, and Tingfa Xu. "Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities." Remote Sensing 11, no. 12 (2019): 1414. http://dx.doi.org/10.3390/rs11121414.

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Image segmentation is extensively used in remote sensing spectral image processing. Most of the existing region merging methods assess the heterogeneity or homogeneity using global or pre-defined parameters, which lack the flexibility to further improve the goodness-of-fit. Recently, the local spectral angle (SA) threshold was used to produce promising segmentation results. However, this method falls short of considering the inherent relationship between adjacent segments. In order to overcome this limitation, an adaptive SA thresholds methods, which combines the inter-segment and boundary hom

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Zhao, Yuefeng, Mengmeng Wu, Liren Zhang, Jingjing Wang, and Dongmei Wei. "An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image." Information 9, no. 10 (2018): 261. http://dx.doi.org/10.3390/info9100261.

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The human face as a biometric trait has been widely used for personal identity verification but it is still a challenging task under uncontrolled conditions. With the development of hyper-spectral imaging acquisition technology, spectral properties with sufficient discriminative information bring new opportunities for a facial image process. This paper presents a novel ensemble method for skin feature segmentation of a hyper-spectral facial image based on a k-means algorithm and a spanning forest algorithm, which exploit both spectral and spatial discriminative features. According to the close

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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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Williams, G. J., M. Aufderheide, K. M. Champley, et al. "Dual-energy fast neutron imaging using tunable short-pulse laser-driven sources." Review of Scientific Instruments 93, no. 9 (2022): 093514. http://dx.doi.org/10.1063/5.0101832.

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A novel dual-energy fast neutron imaging technique is presented using short-pulse laser-driven neutron sources to leverage their inherent adaptive spectral control to enable 3D volume segmentation and reconstruction. Laser-accelerated ion beams incident onto secondary targets create directional, broadband, MeV-class neutrons. Synthetic radiographs are produced of multi-material objects using ion and neutron spectra derived from analytic and numerical models. It is demonstrated that neutron images generated from small changes to the neutron spectra, controlled by altering the initial laser cond

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He, Jing, Gang Liu, Weile Li, Chuan Tang, and Jiayan Lu. "An evaluation approach for segmentation results of high-resolution remote sensing images based on the degree distribution of land cover networks." International Journal of Modern Physics B 32, no. 25 (2018): 1850283. http://dx.doi.org/10.1142/s0217979218502831.

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Identifying the degree distribution of land cover networks is helpful to find analytical methods for characterizing complex land cover, including segmentation techniques of remote sensing images of land cover. After segmentation, we can obtain the geographical objects and corresponding relationships. In order to evaluate the segmentation results, we introduce the concept of land cover network and present an analysis method based on statistics of its degree distribution. Considering the object-oriented segmentation and objects merge-based spectral difference segmentation, we construct the land

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Jiang, Jimao, Diya Sun, Tianbing Wang, and Yuru Pei. "SCCS: Deep Neural Spectral Clustering for Self-Supervised Subcellular Structure Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4003–11. https://doi.org/10.1609/aaai.v39i4.32419.

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Subcellular structure segmentation is a fundamental task in biological imaging. Existing self-supervised representation learning combined with classical k-means clustering achieved unsupervised image segmentation, but it was constrained by time-consuming test-time pixel-wise feature extraction and clustering synchronization. This study introduces SCCS, a lightweight graph neural network-based spectral clustering framework for end-to-end subcellular structure segmentation upon superpixel graphs, greatly relieving the computational complexity in test-time numerical spectral clustering and inter-

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