scholarly journals Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

2021 ◽  
Vol 13 (12) ◽  
pp. 2273
Author(s):  
Xiangtian Meng ◽  
Yilin Bao ◽  
Qiang Ye ◽  
Huanjun Liu ◽  
Xinle Zhang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Prediction Model ◽  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Regional Scale ◽  
Hyperspectral Data ◽  
Recursive Feature Elimination ◽  
Discrete Wavelet ◽  
Denoising Method

In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.

scholarly journals Quality Metrics Evaluation of Hyperspectral Images

2014 ◽  
Vol XL-8 ◽  
pp. 1221-1226 ◽  
Author(s):  
A. K. Singh ◽  
H. V. Kumar ◽  
G. R. Kadambi ◽  
J. K. Kishore ◽  
J. Shuttleworth ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Quality Parameters ◽  
Quality Metrics ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Error Ratio ◽  
Classified Image

In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements of image quality parameters. Experiments were carried out on four different types of hyperspectral images. Aerial and spaceborne hyperspectral images with different spectral and geometric resolutions were considered for quality metrics evaluation. Principal Component Analysis (PCA) has been applied to reduce the dimensionality of hyperspectral data. PCA was ultimately used for reducing the number of effective variables resulting in reduced complexity in processing. In case of ordinary images a human viewer plays an important role in quality evaluation. Hyperspectral data are generally processed by automatic algorithms and hence cannot be viewed directly by human viewers. Therefore evaluating quality of classified image becomes even more significant. An elaborate comparison is made between k-means clustering and segmentation for all the images by taking Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Maximum Squared Error, ratio of squared norms called L2RAT and Entropy. First four parameters are calculated by comparing the quality of original hyperspectral image and classified image. Entropy is a measure of uncertainty or randomness which is calculated for classified image. Proposed methodology can be used for assessing the performance of any hyperspectral image classification techniques.

scholarly journals NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 593-598
Author(s):  
S. Priya ◽  
R. Ghosh ◽  
B. K. Bhattacharya
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Hyperspectral Data ◽  
Correlation Technique ◽  
Spectral Correlation ◽  
Spectral Angle Mapper ◽  
Non Linear

<p><strong>Abstract.</strong> Hyperspectral remote sensing is an advanced remote sensing technology that enhances the ability of accurate classification due to presence of narrow contiguous bands. The large number of continuous bands present in hyperspectral data introduces the problem of computational complexity due to presence of redundant information. There is a need for dimensionality reduction to enhance the ability of users for better characterization of features. Due to presence of high spectral correlation in the hyperspectral datasets, optimum de-correlation technique is required which transforms the hyperspectral data to lower dimensions without compromising with the desirable information present in the data. In this paper, focus has been to reduce the spectral dimensionality problem. So, this research aimed to develop computationally efficient non-linear autoencoder algorithm taking the advantage of non-linear properties of hyperspectral data. The proposed algorithm was applied on airborne hyperspectral image of Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) over Anand region of Gujarat and the performance of the algorithm was evaluated. The Signal-to-Noise Ratio (SNR) increased from 22.78 dB to 48.48 dB with increase in number of nodes in bottleneck layer for reconstruction of image. Spectral distortion was also measured using Spectral Angle Mapper Algorithm (SAM), which reduced from 0.38 to 0.05 with increase in number of nodes in bottleneck layer up to 10. So, this algorithm was able to give good reconstruction of original image from the nodes present in the bottleneck layer.</p>

scholarly journals Variability in <sup>14</sup>C contents of soil organic matter at the plot and regional scale across climatic and geologic gradients

2016 ◽  
Author(s):  
T. S. van der Voort ◽  
F. Hagedorn ◽  
C. McIntyre ◽  
C. Zell ◽  
L. Walthert ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Depth ◽  
Regional Scale ◽  
Mean Annual Temperature ◽  
Spatial And Temporal Variability ◽  
Climatic Parameters ◽  
Soil Carbon Dynamics ◽  
Modelling Assumptions ◽  
Micro Topography

Abstract. Soil organic matter (SOM) forms the largest terrestrial pool of carbon outside of sedimentary rocks. Radiocarbon is a powerful tool for assessing soil organic matter dynamics. However, due to the nature of the measurement, extensive 14C studies of soils systems remain relatively rare. In particular, information on the extent of spatial and temporal variability in 14C contents of soils is limited, yet this information is crucial for establishing the range of baseline properties and for detecting potential modifications to the SOM pool. This study describes a comprehensive approach to explore heterogeneity in bulk SOM 14C in Swiss forest soils that encompass diverse landscapes and climates. We examine spatial variability in soil organic carbon (SOC) 14C, SOC content and C:N ratios over both regional climatic and geologic gradients, on the watershed- and plot-scale and within soil profiles. Results reveal (1) a relatively uniform radiocarbon signal across climatic and geologic gradients in Swiss forest topsoils (0-5 cm, Δ14C=159±36.4, n=12 sites), (2) similar radiocarbon trends with soil depth despite dissimilar environmental conditions, and (3) micro-topography dependent, plot-scale variability that is similar in magnitude to regional-scale variability (e.g., Gleysol, 0-5 cm, Δ14C 126±35.2, n=8 adjacent plots of 10x10m). Statistical analyses have additionally shown that Δ14C signature in the topsoil is not significantly correlated to climatic parameters (precipitation, elevation, primary production) except mean annual temperature at 0-5 cm. These observations have important consequences for SOM carbon stability modelling assumptions, as well as for the understanding of controls on past and current soil carbon dynamics.

Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies

CATENA ◽  
2020 ◽  
Vol 195 ◽  
pp. 104703 ◽  
Author(s):  
Yilin Bao ◽  
Xiangtian Meng ◽  
Susan Ustin ◽  
Xiang Wang ◽  
Xinle Zhang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Prediction Model ◽  
Grouping Strategies

Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity

2011 ◽  
Vol 403-408 ◽  
pp. 866-870
Author(s):  
Vaibhav Nigam ◽  
Smriti Bhatnagar ◽  
Sajal Luthra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Practical Implementation ◽  
Discrete Wavelet ◽  
Maximum Output ◽  
Denoising Method ◽  
Detail Coefficient

This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.

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