A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

X Li, C Li, MM Rahaman, H Sun, X Li, J Wu… - Artificial Intelligence …, 2022 - Springer
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …

[HTML][HTML] A review on graph-based semi-supervised learning methods for hyperspectral image classification

SS Sawant, M Prabukumar - The Egyptian Journal of Remote Sensing and …, 2020 - Elsevier
In this article, a comprehensive review of the state-of-art graph-based learning methods for
classification of the hyperspectral images (HSI) is provided, including a spectral information …

Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework

Z Zhong, J Li, Z Luo, M Chapman - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …

Hyperspectral image classification with deep feature fusion network

W Song, S Li, L Fang, T Lu - IEEE Transactions on Geoscience …, 2018 - ieeexplore.ieee.org
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …

Siamese transformer network for hyperspectral image target detection

W Rao, L Gao, Y Qu, X Sun, B Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Hyperspectral target detection can be described as locating targets of interest within a
hyperspectral image based on prior information of targets. The complexity of actual scenes …

Hyperspectral anomaly detection based on chessboard topology

L Gao, X Sun, X Sun, L Zhuang, Q Du… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Without any prior information, hyperspectral anomaly detection is devoted to locating targets
of interest within a specific scene by exploiting differences in spectral characteristics …

Hyperspectral anomaly detection with relaxed collaborative representation

Z Wu, H Su, X Tao, L Han, ME Paoletti… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …

Efficient inductive vision transformer for oriented object detection in remote sensing imagery

C Zhang, J Su, Y Ju, KM Lam… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Object detection is a fundamental task in remote sensing image analysis and scene
understanding. Previous remote sensing object detectors are typically based on …

Hyperspectral image spatial super-resolution via 3D full convolutional neural network

S Mei, X Yuan, J Ji, Y Zhang, S Wan, Q Du - Remote Sensing, 2017 - mdpi.com
Hyperspectral images are well-known for their fine spectral resolution to discriminate
different materials. However, their spatial resolution is relatively low due to the trade-off in …

[HTML][HTML] Transferable network with Siamese architecture for anomaly detection in hyperspectral images

W Rao, Y Qu, L Gao, X Sun, Y Wu, B Zhang - International Journal of …, 2022 - Elsevier
The purpose of hyperspectral anomaly detection is to distinguish abnormal objects from the
surrounding background. In actual scenes, however, the complexity of ground objects, the …