scholarly journals Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5154 ◽  
Author(s):  
Bo Liu ◽  
Ru Li ◽  
Haidong Li ◽  
Guangyong You ◽  
Shouguang Yan ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Wavelet Coefficients ◽  
Weed Detection ◽  
Imaging Spectrometer ◽  
Spectral Bands ◽  
Spectrometer System ◽  
Field Imaging

Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

scholarly journals Laboratory Calibration of a Field Imaging Spectrometer System

Sensors ◽  
2011 ◽  
Vol 11 (3) ◽  
pp. 2408-2425 ◽  
Author(s):  
Lifu Zhang ◽  
Changping Huang ◽  
Taixia Wu ◽  
Feizhou Zhang ◽  
Qingxi Tong
Keyword(s):  
Imaging Spectrometer ◽  
Laboratory Calibration ◽  
Spectrometer System ◽  
Field Imaging

scholarly journals Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis

2019 ◽  
Vol 11 (10) ◽  
pp. 1219 ◽  
Author(s):  
Lan Zhang ◽  
Hongjun Su ◽  
Jingwei Shen
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Principal Components ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Homogeneous Region ◽  
Kernel Principal Component Analysis ◽  
Nonlinear Features

Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%–0.74%, 3.88%–4.37%, and 0.39%–4.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%–2.68%, 0.12%–1.10%, and 0.01%–0.08%, respectively, when compared with the method based only on the most suitable fundamental image.

scholarly journals A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance

2019 ◽  
Vol 11 (16) ◽  
pp. 1954 ◽  
Author(s):  
Yangjie Sun ◽  
Zhongliang Fu ◽  
Liang Fan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Qualitative Evaluation ◽  
Support Vector ◽  
Spectral Features ◽  
Hyperspectral Image Classification ◽  
Spectral Bands ◽  
Public Data ◽  
Local Covariance ◽  
Classification Pattern

Today, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, such approaches are still hampered by long training times. Traditional spectral–spatial hyperspectral image classification only utilizes spectral features at the pixel level, without considering the correlation between local spectral signatures. Our article has tested a novel hyperspectral image classification pattern, using random-patches convolution and local covariance (RPCC). The RPCC is an effective two-branch method that, on the one hand, obtains a specified number of convolution kernels from the image space through a random strategy and, on the other hand, constructs a covariance matrix between different spectral bands by clustering local neighboring pixels. In our method, the spatial features come from multi-scale and multi-level convolutional layers. The spectral features represent the correlations between different bands. We use the support vector machine as well as spectral and spatial fusion matrices to obtain classification results. Through experiments, RPCC is tested with five excellent methods on three public data-sets. Quantitative and qualitative evaluation indicators indicate that the accuracy of our RPCC method can match or exceed the current state-of-the-art methods.

scholarly journals A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

2021 ◽  
Vol 11 (1) ◽  
pp. 50-55
Author(s):  
Maria Mohammad Yousef ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Fitness Function ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
High Dimensionality ◽  
Support Vector ◽  
Feature Subset

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

scholarly journals CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 411-416
Author(s):  
M. Gu ◽  
S. Lyu ◽  
M. Hou ◽  
S. Ma ◽  
Z. Gao ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Hyperspectral Image ◽  
Research Result ◽  
Principal Component ◽  
Shanxi Province ◽  
Support Vector ◽  
Archaeological Research ◽  
Naked Eye ◽  
Archaeological Work

There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

2021 ◽  
Vol 16 (1) ◽  
pp. 56-74
Author(s):  
Weiwei Yang ◽  
Haifeng Song
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Support Vector ◽  
Svm Classifier ◽  
Spatial Classification ◽  
Homogeneous Regions

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

scholarly journals Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting

2012 ◽  
Vol 1 (1) ◽  
pp. 63 ◽  
Author(s):  
Ankush Chakrabarty ◽  
Olivia Choudhury ◽  
Pallab Sarkar ◽  
Avishek Paul ◽  
Debarghya Sarkar
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Kernel Functions ◽  
Hyperspectral Data ◽  
Superior Performance ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Bacterial Foraging Optimization ◽  
Imaging Spectrometer ◽  
Bacterial Foraging

The present paper describes the development of a hyperspectral image classification scheme using support vector machines (SVM) with spectrally weighted kernels. The kernels are designed during the training phase of the SVM using optimal spectral weights estimated using the Bacterial Foraging Optimization (BFO) algorithm, a popular modern stochastic optimization algorithm. The optimized kernel functions are then in the SVM paradigm for bi-classification of pixels in hyperspectral images. The effectiveness of the proposed approach is demonstrated by implementing it on three widely used benchmark hyperspectral data sets, two of which were taken over agricultural sites at Indian Pines, Indiana, and Salinas Valley, California, by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) at NASA’s Jet Propulsion Laboratory. The third dataset was acquired using the Reflective Optical System Imaging Spectrometer (ROSIS) over an urban scene at Pavia University, Italy to demonstrate the efficacy of the proposed approach in an urban scenario as well as with agricultural data. Classification errors for One-Against-One (OAO) and classification accuracies for One-Against-All (OAA) schemes were computed and compared to other methods developed in recent times. Finally, the use of the BFO-based technique is recommended owing to its superior performance, in comparison to other contemporary stochastic bio-inspired algorithms.

Atmospheric Aerosol Multiband Synthesis Imaging Spectrometer

2018 ◽  
Vol 73 (2) ◽  
pp. 221-228
Author(s):  
Xiaoxu Wang ◽  
Zihui Zhang ◽  
Shurong Wang ◽  
Yu Huang ◽  
Guanyu Lin ◽  
...  
Keyword(s):  
Atmospheric Aerosol ◽  
Optical Design ◽  
Imaging Spectrometer ◽  
Spectral Bands ◽  
Good Imaging ◽  
Filter Array ◽  
Spectrum Distribution ◽  
Solid Foundation ◽  
Aerosol Remote Sensing ◽  
Spectrometer System

According to the characteristics of the spectrum distribution for atmospheric aerosol detection, a multiband synthesis imaging spectrometer system based on Czerny–Turner configuration is designed and proposed in this paper. Using a grating array instead of a traditional single grating, and together with a filter array, the proposed configuration can achieve hyperspectral imaging with the spectral resolution of 0.16 nm, 0.24 nm, 0.29 nm, and 2.05 nm in the spectral bands of 370–430 nm, 640–680 nm, 840–880 nm, and 1560–1660 nm, respectively. First, the system aberration caused by the spectral change was eliminated based on Rowland circle theory; then, Zemax software was used to optimize and analyze the optical design. The analysis results show that the root mean square (RMS) of the spot diagram is < 9 µm in all the working spectral bands, which demonstrates that the aberration has been corrected and a good imaging quality can be achieved. This design of multiband synthesis imaging spectrometer configuration proves to be not only feasible, but also simple and compact, which lays a solid foundation for the practical application in the field of atmospheric aerosol remote sensing spectroscopy.

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