Results 121 to 130 of about 495 (155)

Mamba-convolution hybrid network for underwater image enhancement. [PDF]

Sci Rep
Chen H, Wang Y, Wu L, Hu H, Yan J, Xu H, Lei G.   +6 more
europepmc   +1 more source

Context-aware SAR image ship detection and recognition network. [PDF]

Front Neurorobot
Li C, Yue C, Li H, Wang Z.
europepmc   +1 more source

When Lie Groups Meet Hyperspectral Images: Equivariant Manifold Network for Few-Shot HSI Classification. [PDF]

Sensors (Basel)
Ban H, Feng J, Liu Z, Jiang Y, Wang Z, Liu J, Hu Y, Lin Y.   +7 more
europepmc   +1 more source

Enhanced Leaf Disease Segmentation Using U-Net Architecture for Precision Agriculture: A Deep Learning Approach. [PDF]

Food Sci Nutr
Singh G, Al-Huqail AA, Almogren A, Kaur S, Joshi K, Singh A, Bharany S, Hussen S, Rehman AU.   +8 more
europepmc   +1 more source

Fusion of circulant singular spectrum analysis and multiscale local ternary patterns for effective spectral-spatial feature extraction and small sample hyperspectral image classification. [PDF]

Sci Rep
Wan X, Chen F, Gao W, Mo D, Liu H.
europepmc   +1 more source

Fusion multiscale superpixel features for classification of hyperspectral images

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
A novel multiscale superpixel-based fusion classification approach is proposed for hyperspectral images in this study. Superpixels are considered as basic processing unit for spectral-spatial based classification. The proposed technique consists of three steps.
Xiuping Jia, Hua Wu
exaly   +3 more sources

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Multiscale-Superpixel-Based SparseCEM for Hyperspectral Target Detection

IEEE Geoscience and Remote Sensing Letters, 2022
Jointly exploiting spectral information and spatial information, rather than working on individual pixels, is important for hyperspectral target detection. In this letter, we propose a hyperspectral target detection method relying on superpixel structures of the input image.
Min Zhao, Tiande Gao
exaly   +2 more sources

Multiscale Superpixel-Based Active Learning for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2022
This letter proposes a novel active learning (AL) framework that utilizes the information derived from multiscale superpixel maps for the classification of hyperspectral image. Considering that the nearby pixels with similar spectral properties tend to belong to the same class, we introduce the multiscale superpixel maps for the automatic labeling of ...
Qikai Lu, Lifei Wei
exaly   +2 more sources

Multiscale Superpixel Kernel-Based Low-Rank Representation for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2020
Classification plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel multiscale superpixel kernel-based low-rank representation (MSKLRR) classifier is proposed for HSI classification. A multiscale superpixel segmentation method is first used to generate several homogeneous regions at different scales.
Tianming Zhan, Zhenyu Lu, Minghua Wan
exaly   +2 more sources