Model Updating of Hyperspectral Imaging Data for Variety Discrimination of Maize Seeds Harvested in Different Years by Clustering Algorithm

2016 ◽  
Vol 59 (6) ◽  
pp. 1529-1537 ◽  
Keyword(s):  
Hyperspectral Imaging ◽  
Clustering Algorithm ◽  
Model Updating ◽  
Imaging Data ◽  
Maize Seeds

scholarly journals Study of the Clustering Algorithms for Hyper Spectral Remote Sensing Images

2020 ◽  
Vol 10 (2) ◽  
pp. 117
Author(s):  
Chandrahas Reddy Addanki ◽  
Saraschandrika A ◽  
Viswanadha Reddy A
Keyword(s):  
Hyperspectral Imaging ◽  
Computational Intelligence ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Imaging Data ◽  
Remote Sensing Images ◽  
Data Set ◽  
Spectral Bands ◽  
Hyper Spectral ◽  
Different Types

The data taken from the hyperspectral images are discrete and hard to classify because they are arranged in the contiguous spectral bands. We can easily detect and classify the data from the spectral images if the number of attributes in the images is very little. But it is very difficult to segregate the data from the images if the numbers of classes are more. To make the segregation easy we implement the procedure that utilizes a clustering algorithm. This paper comprises of two sections, firstly to perform unsupervised learning using different types of clustering algorithms and secondly, to compare the efficiency of the resultant clustering of these different methods to prove that which clustering method is best suitable in reading the hyperspectral imaging data. For this I have used these clustering algorithms, they are DBSCAN, MiniBatch K-Means, K-Means. By comparing these techniques I surmised that the K-Means is better for using the HyperSpectral Imaging data. To perform these calculations I used the Matlab data set from the Computational Intelligence Group.

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

Calibration of Hyperspectral Imaging Data: VIRTIS-M Onboard Venus Express

2010 ◽  
Author(s):  
Alejandro Cardesin Moinelo ◽  
Giuseppe Piccioni ◽  
Eleonora Ammannito ◽  
Gianrico Filacchione ◽  
Pierre Drossart
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Data ◽  
Venus Express

Robust Blind Image Fusion for Misaligned Hyperspectral Imaging Data

PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Leon Bungert ◽  
Matthias J. Ehrhardt ◽  
Rafael Reisenhofer
Keyword(s):  
Image Fusion ◽  
Hyperspectral Imaging ◽  
Imaging Data ◽  
Blind Image
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