Results 51 to 60 of about 15,264 (300)

Hyperspectral colon tissue cell classification [PDF]

, 2004
A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals ...
Rajpoot, Nasir M. (Nasir Mahmood), Turner, Martin J., Rajpoot, Kashif   +2 more
core  

In Situ Pseudo‐Halide Diffusion Enables Buried Interface Regulation and Crystallinity Enhancement in Perovskite Solar Cells

Advanced Science, EarlyView.
Thermally activated LiHCOO induces in situ pseudo‐halide diffusion, promoting buried interface strain release and perovskite crystallization. The monoclinic LiHCOO phase forms an open framework structure that enhances HCOO− diffusion and drives interfacial restructuring.
Chao Gao, Li He, Changjiang Li, Long Fang, Wenzhong Shen   +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 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

EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier.
P.-H. Hsu
doaj   +1 more source

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

Classification techniques for hyperspectral remote sensing [PDF]

, 2011
This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how ...
Kam, Firmin
core  

MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network

, 2023
Classification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral ...
Wu, Guilu, Ullah Bazai, Sibghat, NEIRA MOLINA, HAROLD ROBERTO, Marjan, Shah, Aslam Bhatti, Uzair, Huang, Mengxing, Baryalai, Mehmood, Tang, Hao   +7 more
core   +1 more source

Segmentation-Aware Hyperspectral Image Classification

CoRR, 2019
To appear at International Geoscience and Remote Sensing Symposium (IGARSS ...
Berkan Demirel, Omer Ozdil, Yunus Emre Esin, Safak Ozturk   +3 more
openaire   +2 more sources

Is Pretraining Necessary for hyperspectral image classification? [PDF]

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pre-training effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that ...
Hyungtae Lee, Sungmin Eum, Heesung Kwon
openaire   +2 more sources