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Enhancement and analysis of hyperspectral satellite images for Soil Study and Behavior

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Abstract

Using soil maps at the proper scales can help with environmental and agricultural decision-making. In this way, soil properties have been investigated using remote sensing technologies, although, the assessment of their spatial information may be limited by the existence of objects other than soil. This problem has been addressed by soil researchers who have been analyzing the spatial patterns via hyperspectral satellite images in an effort to enhance the soil property maps. In this research, we introduced a four-step model named Enhancement and Analysis of Hyperspectral Satellite Images for Soil Study and Behavior (EAHSB) which includes the steps like preprocessing, segmentation, feature extraction, and classification. The work starts with the preprocessing of the input image via improved anisotropic filtering, which enhances the image to process further. Subsequently, the segmentation takes place by the improved spatial constraint kernel Fuzzy c-means (KFCM) to segment the Region of Interest (ROI) and non-Region of interest (ROI) regions. For the study of soil behavior classification, the vegetation indices-based features are extracted including Transformed Vegetation Index (TVI), Radar Vegetation index (RVI), soil adjusted vegetation index (SAVI), Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), modified soil adjusted vegetation index (MSAVI), and Modified Soil Ratio (MSR) as well. Finally, the soil behavior classification is carried out based on the extracted features via a hybrid model combining Deep Residual Network (DRN) and Improved Recurrent Neural Network (RNN) with new layer structure on attaining the classification outcomes like Soil Moister Index Forecast (SMI), Soil Vegetation Index (SVI), and pH, respectively.

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Data availability

“Here, the total number of images used is a hundred. Of that 25 images is from Bellary, the next 25 images are taken from Chamaraja Nagar, 10 images are from Jodhpur, another 10 images are from Lucknow, 15 images are taken from Delhi and finally the last 15 images are taken from the state Gujarat. Similarly, the images are collected from Landsatcollect2level2”.

Abbreviations

SOC:

Soil Organic Carbon

DSR:

Dynamic Stochastic Resonance

CGWO:

Chaotic Grey Wolf Optimization

NSST:

Non-Sub Sampled Shearlet Transform Domain

NSCT:

Nonsubsampled Contourlet Transform

SDS:

Stochastic Diffusion Research

THM:

Thermo Hydraulic-Mechanical

ALEM:

Analytical Layer Element Method

FEM:

Finite Element Method

ABCETP:

Ameliorated Balance Contrast Enhancement Technique using a Parabolic

CNN:

Convolutional Neural Network

KFCM:

Kernel Fuzzy c-means

SRAD:

Speckle reducing Anisotrophic Diffusion

ICOV:

Instantaneous Coefficient Of Variation

ML:

Machine Learning

RBF:

Radial Basis Function

DL:

Deep Learning

FCM:

Fuzzy c-means

VRT:

Variable Rate Application

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Author notes

  1. Amit Mittal

    Present address: Chitkara Business School, Chitkara University, Punjab, India

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Varun Malik, Ruchi Mittal, Amandeep Kaur & Geetanjali Singla

  2. Chitkara Business School, Chitkara University, Punjab, India

    Manjinder Singh

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  1. Varun Malik
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All writers have contributed significantly to the concept and layout, text modifying, and final approval of the published version. It was also decided that each author would be accountable for all aspects of the work and guarantee that any doubts regarding the veracity or integrity of any section of the work would be properly looked into and resolved.

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Malik, V., Mittal, R., Kaur, A. et al. Enhancement and analysis of hyperspectral satellite images for Soil Study and Behavior. Multimed Tools Appl 83, 33879–33902 (2024). https://doi.org/10.1007/s11042-023-16729-4

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