Results 51 to 60 of about 49,813 (252)

Masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images

Remote Sensing, 2023
The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods.
Wenkai Liu, Bing Liu, Peipei He, Qingfeng Hu, Kuiliang Gao, Hui Li   +5 more
doaj   +1 more source

A scalable dataflow accelerator for real time onboard hyperspectral image classification

, 2016
© Springer International Publishing Switzerland 2016.Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications.
C Gustavo, C Kyrkou, F Melgani, JM Bioucas-Dias, K Christos, M Papadonikolakis, S Lopez, Y Liu, Z Xue   +8 more
core   +1 more source

Single‐Cell Metabolic Imaging and Digital Scoring of Fat Tissue Remodeling by Label‐Free Metabolic Microscopy

Advanced Science, EarlyView.
Mid‐infrared optoacoustic microscopy (MiROM) acquires lipid‐ and protein‐ associated vibrational contrast in intact fat tissue without dyes, preserving native tissue architecture. Through lateral and axial segmentation, MiROM tracks intrinsic intracellular changes during postnatal remodeling. A quantitative spatial analysis tool (Q‐SAT) maps white‐ and
Myeongseop Kim, Constantin Berger, Alexander Wolf, Alina Peteranderl, Martin Klingenspor, Vasilis Ntziachristos, Yongguo Li, Miguel A. Pleitez   +7 more
wiley   +1 more source

DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification

Applied Sciences
In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress.
Laiying Fu, Xiaoyong Chen, Yanan Xu, Xiao Li   +3 more
doaj   +1 more source

A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

Remote Sensing, 2020
Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult.
Binge Cui, Jiandi Cui, Yan Lu, Nannan Guo, Maoguo Gong   +4 more
doaj   +1 more source

An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data

ISPRS International Journal of Geo-Information, 2021
Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation.
Shiuan Wan, Mei-Ling Yeh, Hong-Lin Ma
doaj   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Efficient Hyperspectral Image Classification Using Discrete Cosine Transform on Limited-Resource Systems

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep learning-based approaches to hyperspectral image analysis have attracted large attention and exhibited high performance in image classification tasks. However, deployment of deep learning-based hyperspectral image analysis systems is challenging due
Eungjoo Lee, Lei Pan, Zhu Li, Shuvra S. Bhattacharyya   +3 more
doaj   +1 more source

A Spectral Spatial Attention Fusion with Deformable Convolutional Residual Network for Hyperspectral Image Classification

Remote Sensing, 2021
Convolutional neural networks (CNNs) have exhibited excellent performance in hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it is difficult to achieve high classification accuracy of hyperspectral images with ...
Tianyu Zhang, Cuiping Shi, Diling Liao, Liguo Wang   +3 more
doaj   +1 more source

Ink Classification in Hyperspectral Images

, 2020
Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the ...
Bilal, Muhammad, Ahmad, Haris, Ali, Muhammad Khizer   +2 more
openaire   +2 more sources