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A review of convolutional neural networks in computer vision
In computer vision, a series of exemplary advances have been made in several areas
involving image classification, semantic segmentation, object detection, and image super …
involving image classification, semantic segmentation, object detection, and image super …
A review of machine learning and deep learning for object detection, semantic segmentation, and human action recognition in machine and robotic vision
Machine vision, an interdisciplinary field that aims to replicate human visual perception in
computers, has experienced rapid progress and significant contributions. This paper traces …
computers, has experienced rapid progress and significant contributions. This paper traces …
Dual-branch spectral–spatial attention network for hyperspectral image classification
In order to achieve accurate hyperspectral image (HSI) classification, the convolutional
neural network (CNN) has been extensively utilized. However, most existing patch-based …
neural network (CNN) has been extensively utilized. However, most existing patch-based …
3D residual spatial–spectral convolution network for hyperspectral remote sensing image classification
Hyperspectral remote sensing images (HRSI) are 3D image cubes that contain hundreds of
spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis …
spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis …
Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many
contiguous narrow spectral wavelength bands. For its efficient thematic map** or …
contiguous narrow spectral wavelength bands. For its efficient thematic map** or …
Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review
Over the last few years, deep learning (DL) techniques have gained popularity and have
become the new standard for data processing in remote sensing analysis. Deep learning …
become the new standard for data processing in remote sensing analysis. Deep learning …
SSTNet: spatial, spectral, and texture aware attention network using hyperspectral image for corn variety identification
Currently, most existing methods using hyperspectral images to assist seed identification
only consider the spectral information but ignore the spatial information resulting in …
only consider the spectral information but ignore the spatial information resulting in …
Spatial-spectral classification of hyperspectral remote sensing images using 3D CNN based LeNet-5 architecture
Hyperspectral remote sensing image (HRSI) analysis are commonly used in a wide variety
of remote sensing applications such as land cover analysis, military surveillance, object …
of remote sensing applications such as land cover analysis, military surveillance, object …
[HTML][HTML] Risk assessment for cropland abandonment in mountainous area based on AHP and PCA—Take Yunnan Province in China as an example
Y Ma, J Wang, J **ong, M Sun, J Wang - Ecological Indicators, 2024 - Elsevier
Assessing the risk of cropland abandonment in mountainous area is essential for ensuring
food security. However, the current evaluation indicator system is incomplete, and the …
food security. However, the current evaluation indicator system is incomplete, and the …
[HTML][HTML] A survey of computer vision techniques for forest characterization and carbon monitoring tasks
Estimation of terrestrial carbon balance is one of the key tasks in the understanding and
prognosis of climate change impacts and the development of tools and policies according to …
prognosis of climate change impacts and the development of tools and policies according to …