Results 21 to 30 of about 495 (155)
Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation
Remote Sensing, 2018 High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to ...
Tianming Zhan +5 more
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Remote Sensing, 2019 Automatic building extraction using a single data type, either 2D remotely-sensed images or light detection and ranging 3D point clouds, remains insufficient to accurately delineate building outlines for automatic mapping, despite active research in this
Haiqing He +5 more
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Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification
Remote SensingWith the development of hyperspectral sensors, the availability of hyperspectral images (HSIs) has increased significantly, prompting advancements in deep learning-based hyperspectral image classification (HSIC) methods.
Ke Wu, Yanting Zhan, Ying An, Suyi Li
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Land use recognition from multispectral satellite images is fundamentally critical for geological applications, but the results are not satisfied.
Yaobin Ma, Xiaohua Deng, Jingbo Wei
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingPolarimetric synthetic aperture radar (PolSAR) has attracted more attentions because of its excellent observation ability, and PolSAR image classification has become one of the significant tasks in remote sensing interpretation.
Ru Wang, Yinju Nie, Jie Geng
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Multiscale NMF based on intra-pixel and inter-pixel structure adjustment for spectral unmixing
International Journal of Applied Earth Observations and GeoinformationVarious improved nonnegative matrix factorization (NMF) methods have been widely used in spectral unmixing (SU), including nonlinear versions to counter for the lower spatial resolution and interaction between materials.
Tingting Yang +3 more
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Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
Applied Sciences, 2019 Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold.
Lin He +3 more
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Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral unmixing is a critical challenge in the analysis of hyperspectral remote sensing data. Due to the complex interactions between incident light and materials, which are significantly influenced by the three-dimensional geometry of the scene ...
Xiu Zhao, Meiping Song
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Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning [PDF]
The Scientific World Journal, 2015 Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information.
Yuan Xu +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBy focusing on the structure exploration and information propagation from non-Euclidean data space, graph convolutional neural network (GCN), which can extract abundant and discriminative features, has been a valuable topic in polarimetric synthetic ...
Ze-Chen Li +4 more
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