High resolution urban monitoring using neural network and transform algorithms

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover from space. Satellite observations are carried out regularly and continuously and provide a great deal of information on land cover over large areas. High spatial resolution imagery makes it possible to overcome the “mixed-pixel” problem inherent in more moderate resolution satellite sensors. At the same time, high-resolution images present a new challenge over other satellite systems since a relatively large amount of data must be analyzed, processed, and classified in order to characterize land cover features and to produce classification maps. Actually, in spite of the great potential of remote sensing as a source of information on land cover and the long history of research devoted to the extraction of land cover information from remotely sensed imagery, many problems have been encountered, and the accuracy of land cover maps derived from remotely sensed imagery has often been viewed as too low for operational users. This study focuses on high resolution urban monitoring using Neural Network (NN) analyses for land cover classification and change detection, and Fast Fourier Transform (FFT) evaluations of wavenumber spectra to characterize the spatial scales of land cover features. The contributions of the present work include: classification and change detection for urban areas using NN algorithms and multi-temporal very high resolution multi-spectral images (QuickBird, Digital Globe Co.); development and implementation of neural networks apt to classify a variety of multi-spectral images of cities arbitrarily located in the world; use of different wavenumber spectra produced by two-dimensional FFTs to understand the origin of significant features in the images of different urban environments subject to the subsequent classification; optimization of the neural net topology to classify urban environments, to produce thematic maps, and to analyze the urbanization processes. This work can considered as a first step in demonstrating how NN and FFT algorithms can contribute to the development of Image Information Mining (IMM) in Earth Observation

Earth imaging with microsatellites: An investigation, design, implementation and in-orbit demonstration of electronic imaging systems for earth observation on-board low-cost microsatellites.

This research programme has studied the possibilities and difficulties of using 50 kg microsatellites to perform remote imaging of the Earth. The design constraints of these missions are quite different to those encountered in larger, conventional spacecraft. While the main attractions of microsatellites are low cost and fast response times, they present the following key limitations: Payload mass under 5 kg, Continuous payload power under 5 Watts, peak power up to 15 Watts, Narrow communications bandwidths (9.6 / 38.4 kbps), Attitude control to within 5°, No moving mechanics. The most significant factor is the limited attitude stability. Without sub-degree attitude control, conventional scanning imaging systems cannot preserve scene geometry, and are therefore poorly suited to current microsatellite capabilities. The foremost conclusion of this thesis is that electronic cameras, which capture entire scenes in a single operation, must be used to overcome the effects of the satellite's motion. The potential applications of electronic cameras, including microsatellite remote sensing, have erupted with the recent availability of high sensitivity field-array CCD (charge-coupled device) image sensors. The research programme has established suitable techniques and architectures necessary for CCD sensors, cameras and entire imaging systems to fulfil scientific/commercial remote sensing despite the difficult conditions on microsatellites. The author has refined these theories by designing, building and exploiting in-orbit five generations of electronic cameras. The major objective of meteorological scale imaging was conclusively demonstrated by the Earth imaging camera flown on the UoSAT-5 spacecraft in 1991. Improved cameras have since been carried by the KITSAT-1 (1992) and PoSAT-1 (1993) microsatellites. PoSAT-1 also flies a medium resolution camera (200 metres) which (despite complete success) has highlighted certain limitations of microsatellites for high resolution remote sensing. A reworked, and extensively modularised, design has been developed for the four camera systems deployed on the FASat-Alfa mission (1995). Based on the success of these missions, this thesis presents many recommendations for the design of microsatellite imaging systems. The novelty of this research programme has been the principle of designing practical camera systems to fit on an existing, highly restrictive, satellite platform, rather than conceiving a fictitious small satellite to support a high performance scanning imager. This pragmatic approach has resulted in the first incontestable demonstrations of the feasibility of remote sensing of the Earth from inexpensive microsatellites

Synthesis of new pyrazolium based tunable aryl alkyl ionic liquids and their use in removal of methylene blue from aqueous solution

In this study, two new pyrazolium based tunable aryl alkyl ionic liquids, 2-ethyl-1-(4-methylphenyl)-3,5- dimethylpyrazolium tetrafluoroborate (3a) and 1-(4-methylphenyl)-2-pentyl-3,5-dimethylpyrazolium tetrafluoroborate (3b), were synthesized via three-step reaction and characterized. The removal of methylene blue (MB) from aqueous solution has been investigated using the synthesized salts as an extractant and methylene chloride as a solvent. The obtained results show that MB was extracted from aqueous solution with high extraction efficiency up to 87 % at room temperature at the natural pH of MB solution. The influence of the alkyl chain length on the properties of the salts and their extraction efficiency of MB was investigated

Actions for Bioenergy and Biofuels: A Sustainable Shift

The topic of bioenergy is a multidisciplinary one, where the use of resources and skills can be optimized for the development of sustainable models. It is a time for green strategies, but also for action. It is, therefore, necessary to implement projects that address virtuous examples of the circular bioeconomy. All politicians are called on to contribute, because this global goal can only be achieved if a contribution is made by all countries

Frequency domain filtering strategies for hybrid optical information processing

Due to the rapid development of spatial light modulators, optical materials and filter design techniques; real time pattern recognition exploiting hybrid optical correlation is increasingly attractive. The spatial light modulator (SLM) enables signal or image patterns to be encoded as amplitude and/or phase modulation patterns across a directed coherent optical beam. It is the vast computational potential of optical information processing that provides the motivation for the design of spatial filters suitable for implementation on currently available SLMs. A good correlation filter should produce a sharp localised correlation peak in the output plane, and be able to achieve this in the presence of noise in the input plane. Thus optimisation is of great importance in optical correlator systems. The lower frequency components produce a broad correlation peak, whereas the higher frequency-band produces a sharp correlation peak that is sensitive to noise. This suggests that a filter with a band-pass characteristic can be made tolerant to noise and also give good localisation of the correlation peak. Here, spatial frequency band tuning and adaptive filtering are developed for this purpose. Spatial frequency selectivity is found to be very important for the design of a spatial filter, a compromise between correlation peak sharpness and noise robustness is sought. Thus, the tuneable photorefractive filter is assessed and difference of Gaussian function filter is developed. For different noise characteristics the spatial filter parameters must be tuned to give optimised performance, this optimisation process depends greatly on the noise and target object spectral characteristics. Adaptive filter design is developed which integrates the phase only filter with the classical matched filter, where a variable amplitude threshold value is set so that, at a particular spatial pixel location, if the amplitude value is greater than the pre-set threshold, only phase information is recorded; otherwise, both the phase and amplitude information are encoded. The development of the synthetic discriminant function filters as distortion tolerant filters was motivated by the sensitivity of the spatial matched filters to distortions in the input image such as in-plane rotations, out-of-plane rotations and scale variations. In applications it is very important that a spatial filter detects the target object from the input scene regardless of its orientation. The design of synthetic discriminant function filters suitable for implementation on commercially available SLM's is an extremely important feature of current research in the area. Therefore, based on the filter synthetic discriminant function (fSDF), a modified filter synthetic discriminant function filter is developed. Via the filter modulation operator Ml the modified fSDF permits advantageous preprocessing of individual training set images that are used in a linear combination to construct the fSDF, which applies a modulation operator M to the synthetic discriminant function. A relaxation algorithm is used to satisfy the equal correlation peaks rule in the correlator output plane. As the filter modulation operators M and M can be given any functional form, the MfSDF design proposed herein is sufficiently general to be described as a unified filter modulation SDF design. By considering the implementation of the modified fSDF on currently available SLM's, the binary phase-only encoded and the multilevel phase and amplitude encoded modified fSDF, which are suitable for the binary mode SLM and the liquid crystal television respectively, are investigated and evaluated. The evaluation is performed to better understand the image distortion range that can be encoded using the modified fSDF filters. The Wiener filter, which has been used extensively for the image restoration and signal processing, is developed for robust optical pattern recognition and classification. The Wiener filter is formulated to incorporate the in-class image (to be detected) and the out-of-class noise image (to be rejected) into a single step filter construction. A Wiener filter-SDF is thus developed and investigated by applying it to vehicle recognition and laser cutting process control. The joint transform correlator (JTC) provides a popular alternative to the Van- derLugt architecture. To improve its performance, a modified fringe-adjusted filter based JTC is introduced and with a multi-object input shown to ameliorate the noise sensitivity of the fringe-adjusted filter based JTC; this provides a solution that overcomes the difficulties encountered with binary JTC techniques. In order to permit the JTC to accommodate a high degree of image distortion, a SDF based modified fringe-adjusted JTC is developed and investigated to illustrate its ability to deal with noisy multi-class, multi-object inputs