A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
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 …
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 …
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
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 …
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …
Hyperspectral image classification with deep feature fusion network
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 …
achieved good performance. In general, deep models adopt a large number of hierarchical …
Siamese transformer network for hyperspectral image target detection
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 image based on prior information of targets. The complexity of actual scenes …
Hyperspectral anomaly detection based on chessboard topology
Without any prior information, hyperspectral anomaly detection is devoted to locating targets
of interest within a specific scene by exploiting differences in spectral characteristics …
of interest within a specific scene by exploiting differences in spectral characteristics …
Hyperspectral anomaly detection with relaxed collaborative representation
Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …
abundant spectral and spatial information contained in hyperspectral images. Recently …
Efficient inductive vision transformer for oriented object detection in remote sensing imagery
Object detection is a fundamental task in remote sensing image analysis and scene
understanding. Previous remote sensing object detectors are typically based on …
understanding. Previous remote sensing object detectors are typically based on …
Hyperspectral image spatial super-resolution via 3D full convolutional neural network
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 …
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
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 …
surrounding background. In actual scenes, however, the complexity of ground objects, the …