ArticleRSC advances2022
Effective band selection of hyperspectral image by an attention mechanism-based convolutional network.
Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 30 citations in OpenAlex.
- BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.Plants (Basel, Switzerland) · 2024Article
- DCPlant phenomics (Washington, D.C.) · 2024Article
- Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation.Frontiers in plant science · 2024Article
- Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.Foods (Basel, Switzerland) · 2022Article
- Analysis of the Basic Characteristics and Teaching Environment and Mode of Music Appreciation Course Based on Core Literacy.Journal of environmental and public health · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The selection of effective and representative spectral bands is extremely important in eliminating redundant information and reducing the computational burden for the potential real-time applications of hyperspectral imaging. However, current band selection methods act as a separate procedure before model training and are implemented merely based on extracted average spectra without incorporating spatial information. In this paper, an end-to-end trainable network framework that combines band selection, feature extraction, and model training was proposed based on a 3D CNN (convolutional neural network, CNN) with the attention mechanism embedded in its first layer. The learned band attention vector was adopted as the basis of a band importance indicator to select effective bands. The proposed network was evaluated by two datasets, a regression dataset for predicting the relative chlorophyll content (soil and plant analyzer development, SPAD) of basil leaves and a classification dataset for detecting the drought stress of pepper leaves. A number of calibration models, including SVM, 1D-CNN, 2B-CNN (two-branch CNN), 3D ResNet and the developed network were established for performance comparison. Results showed that the effective bands selected by the proposed attention-based model achieved higher regression
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.