Wavelet analysis of hyperspectral reflectance data for spectral feature extraction

2005 ◽  
Author(s):  
Guiling Sun ◽  
Yonghua Fang ◽  
Cuilan Zhang ◽  
Xianbing Wang ◽  
Benyong Yang
Keyword(s):  
Feature Extraction ◽  
Wavelet Analysis ◽  
Spectral Feature ◽  
Hyperspectral Reflectance ◽  
Reflectance Data

scholarly journals Spectral–Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression

2020 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Fanqiang Kong ◽  
Kedi Hu ◽  
Yunsong Li ◽  
Dan Li ◽  
Shunmin Zhao
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Multispectral Imaging ◽  
Rapid Development ◽  
Spectral Feature ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spatial Feature ◽  
Spatial Features ◽  
Multispectral Image Compression

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

Analysis of hyperspectral reflectance data for monitoring growth and development of lesquerella

2011 ◽  
Vol 33 (2) ◽  
pp. 524-531 ◽  
Author(s):  
K.R. Thorp ◽  
D.A. Dierig ◽  
A.N. French ◽  
D.J. Hunsaker
Keyword(s):  
Growth And Development ◽  
Hyperspectral Reflectance ◽  
Reflectance Data

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

scholarly journals Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco

2021 ◽  
Author(s):  
Austin Hayes ◽  
T. David Reed
Keyword(s):  
Machine Learning ◽  
Broad Band ◽  
Phytophthora Nicotianae ◽  
Hyperspectral Reflectance ◽  
Black Shank ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Band Index ◽  
Intensively Managed ◽  
Reflectance Data

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.

Multi-Layer Perceptron Pattern Recognition of Handwriting Ink Based on PLS-DA Raman Spectral Feature Extraction

2021 ◽  
Vol 58 (1) ◽  
pp. 0130002
Author(s):  
王晓宾 Wang Xiaobin ◽  
马枭 Ma Xiao ◽  
杨蕾 Yang Lei ◽  
李春宇 Li Chunyu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Spectral Feature ◽  
Multi Layer Perceptron ◽  
Raman Spectral
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