PhD Theses

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/2

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Start date: 2020Subject: search.filters.subject.Remote sensing

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    ItemOpen Access
    Heavy Metal Detection in Crops and Soil Clay Mineral Abundance Mapping using Hyperspectral Data
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Priya, Swati; Ghosh, Ranendu; Mandal, Srimanta
    Presence of heavy metal in crops is an indicator of environmental pollution. Theheavy metals found in the plant indicate that the specific metal exists in the terrestrialenvironment. These metals affect leaves� spectral characteristics and interferewith plants� biochemical features, such as chlorophyll concentration and photosynthesis.Accurate detection of heavy metals in plants is necessary for agriculturalmanagement and to preserve ecological balance. Field spectroscopy techniquesare used to measure the spectral changes triggered due to contaminationwith heavy metals. The advantage of these remote sensing approaches to assessheavy metal contamination is that they can frequently collect data across a widegeographic area.The study mainly focuses on detecting different levels of heavy metal pollutionfrom airborne hyperspectral data using reference data from in situ controlledpot experiments. We constructed a training set spectrum from a controlled experimenton cotton and tobacco for two important heavy metals, Lead (Pb) andCadmium (Cd). Cotton and tobacco crops were grown in pots after artificiallycontaminating the soil with four Pb and Cd heavy metal treatments. The hyperspectraland biochemical data generated spectra of heavy metal concentrations atdifferent crop growth stages. Standard reflectance spectra at different contaminationlevels do not show significant changes at different wavelengths due to thepresence of heavy metal. These spectra were further decomposed using wavelettransform at different levels to capture the subtle changes in spectra using thedetailed component of wavelets. The reconstructed detailed wavelet reflectanceat the third level of decomposition was found to be significant with heavy metalstress. The correlation analysis established that the wavelength range of 651-742nano meter (nm) in cotton was sensitive to Pb stress, and 631-802 nm was sensitiveto Cd stress in tobacco. The reconstructed detail reflectance at a particularwavelength was then further used as reference spectra with different heavy metallevels to map heavy metal pollution.The AVIRIS-NG data obtained for the study area was first classified to identifythe tobacco crop in the Anand region and the cotton crop in the Surendranagarregion using a combination of Autoencoder (AE) for feature extraction followedby an artificial neural network for classification. The training data obtained fromthe pot experiment were utilized to map Pb and Cd pollution from classified airbornehyperspectral data from Airborne Visible InfraRed Imaging Spectrometer- Next Generation (AVIRIS-NG) using a spectral matching algorithm known asDynamic Spectral Warping (DSW). The results confirm the efficiency of the developedalgorithm in estimating Cd content in tobacco and Pb content in cottoncrops. The model was validated by collecting the exact field points and heavymetal concentration, which shows a promising result for this algorithm.Diverse soil minerals may be easily identified through modern hyperspectraltechnology for remote sensing. The aerial hyperspectral sensor�s enhanced spatialand spectral resolution can identify the abundance of several clay minerals, suchas Kaolinite, Montmorillonite, and Illite. This study maps the clay mineral distributionin the Udaipur area of Rajasthan and the Ambaji region of Gujarat usinghyperspectral data acquired by the AVIRIS-NG sensor on an airborne platform.The representative soil sampling sites were selected from hyperspectral datausing the Spectral Feature Fitting (SFF) algorithm. X-ray Diffraction (XRD) analysiswas carried out to find different clay minerals in the samples. Then the regressionanalysis was carried out to find the relation between Absorption PeakDepth (APD) extracted from hyperspectral data corresponding to the actual locationof sampling sites and the corresponding clay percentage obtained from XRDanalysis. Regression analysis between absorption peak depth values estimatedfrom hyperspectral data at 2205 nm � 2214 nm spectral region of soil samplingsites and corresponding clay content value showed a significant relationship. Theregression line obtained for the known pixel is used to prepare the mineral abundance map over the study area. The study over the Udaipur region shows thedominance of montmorillonite clay minerals, and the Ambaji region showed anabundance of kaolinite.
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    ItemOpen Access
    Classification Techniques of PolSAR Images
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2021) Gadhiya, Tusharkumar Damjibhai; Roy, Anil K.
    Over the years, optical remote sensing technology has restricted the ability to capture images during harsh weather settings and at night time. However, Synthetic Aperture Radar (SAR) is independent of solar illumination and thus allows allweather continuous earth monitoring capability. A polarimetric synthetic aperture radar (PolSAR) is one type of SAR image which captures different attributes of the target by combining four different polarization states. Some PolSAR systems such as E-SAR, AIRSAR, F-SAR, etc., can capture abundant information of the target by employing multifrequency bands simultaneously providing rich information of the target. It makes SAR images suitable in wide range of Earth observation applications such as change detection, object detection, monitoring, classification, etc. This thesis addresses the classification problem of single frequency and multifrequency PolSAR images. PolSAR image classification is primarily a pixel based classification problem where our goal is to assign a label to each pixel of the image. Unlike optical images, PolSAR images are complex in nature which limits our ability of direct visual interpretation. Due to its active imaging nature, SAR images suffers from speckle noise which hinders the performance of pixel based classification. To address the classification problem, five contributions related to improving classification time and accuracy are discussed. We will begin with the introduction of Optimized Wishart Network (OWN) which is an improvement over the existing Wishart Network (WN) for the classification of single frequency PolSAR images. We propose methods to improve the classification time by reducing the computation overhead in WN and improve classification accuracy by proposing a better weight initialization method. Next, we propose the extended OWN (e-OWN) for classification of multifrequency PolSAR data. We show that the proposed method is able to combine different band information effectively and produces better classification accuracy. One of the big challenge for pixel based Pol-SAR image classification method is the presence of speckle noise in the image. To tackle that, we propose the superpixel driven OWN which uses both pixel and superpixel information to handle the noisy pixels. Finally, we present an stacked autoencoder based classification of multifrequency PolSAR images. All the proposed approaches are tested on variety of single frequency and multifrequency PolSAR datasets.