Results 21 to 30 of about 417 (152)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming remote sensing applications. However, for artificial-intelligence-guided tasks, such as land cover mapping and ground-object mapping, most deep-learning-based architectures
Tanmay Kumar Behera +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The convolutional neural network (CNN) has a poor performance in nonuniform and edge regions due to the limitation of fixed receptive field. At the same time, feature stacking of input data can bring burden and overfitting to the network.
Ronghua Shang +5 more
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Image classification using graph neural network and multiscale wavelet superpixels
Pattern Recognition Letters, 2023 Prior studies using graph neural networks (GNNs) for image classification have focused on graphs generated from a regular grid of pixels or similar-sized superpixels. In the latter, a single target number of superpixels is defined for an entire dataset irrespective of differences across images and their intrinsic multiscale structure.
Varun Vasudevan +3 more
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Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF
Remote Sensing, 2023 The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing ...
Xiang Cheng, Hong Lei
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MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM SUPERPIXELS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Abstract. Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical.
F. Lang, J. Yang, L. Wu, D. Li, D. Li
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International Journal of Digital Earth, 2022 In map multiscale visualization, typification is the process of replacing original objects, such as buildings, using a smaller number of objects while maintaining initial geometrical and distribution characteristics.
Yilang Shen +4 more
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A Superpixel-Based Relational Auto-Encoder for Feature Extraction of Hyperspectral Images
Remote Sensing, 2019 Filter banks transferred from a pre-trained deep convolutional network exhibit significant performance in heightening the inter-class separability for hyperspectral image feature extraction, but weakening the intra-class consistency simultaneously.
Miaomiao Liang, Licheng Jiao, Zhe Meng
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Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
Remote Sensing, 2017 Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification.
Fei Tong +3 more
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Research on land use classification of hyperspectral images based on multiscale superpixels
Mathematical Biosciences and Engineering, 2020 With the rapid development of remote sensing technology, research on land use classification methods based on hyperspectral remote sensing images has attracted widespread attention. Existing land-use classification studies mostly use the average filtering method at a single scale for spectral image processing. These methods cannot accurately filter the
Hua Wang +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Although deep learning-based methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image classification tasks, most of the available techniques are not suitable to deal with PolSAR data on irregular domains, e.g ...
Shijie Ren, Feng Zhou
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