Optimisation de la configuration d'un instrument superspectral aéroporté pour la classification : application au milieu urbain

This work was performed in the context of a possible enrichment of land cover databases. The description of land cover is necessary it possible to produce environmental indicators for the management of ecosystems and territories, in response to various societal and scientific needs. Thus, different land cover databases already exist at various levels (global, European, national, regional or local) or are currently being produced. However, it appeared that knowledge about land cover should more detailled in urban areas, since it is required by several city modeling applications (micro-meteorological, hydrological, or pollution monitoring simulators), or public regulations monitoring (e.g. concerning ground perviousness). Such materials maps would be (both semantically and spatially) finer than what is contained in existing land cover databases. Therefore, they could be an additional layer, both in land cover databases (such as in IGN High Resolution land cover database) and in 3D city models. No existing database contains such information about urban material maps. Thus remote sensing is the only solution to produce it. However, due to the high heterogeneity of urban materials, their variability, but also the strong similarities between different material classes, usual optical multispectral sensors (with only the 4 red - green - blue - near infrared bands) are not sufficient to reach a good discrimination of materials. A multispectral sensor or superspectral, that is to say spectrally richer, could therefore provide a solution to this limit. Thus, this work was performed intending the design of such sensor. It aimed at identifying the best spectral configuration for classification of urban materials, or at least to propose sub-optimal solutions. In other words, a spectral optimization was carried out in order to optimize both the position of the bands in the spectrum and their width. Automatic feature selection methods were used. This work was performed in two steps. A first task aimed at defining the spectral optimization methods and at validating them on literature reference data sets. Two state-of-the-art optimization heuristics (Sequential Forward Floating Search and genetic algorithms) were chosen owing to their genericity and flexibility, and therefore their ability to be used to optimize different feature selection criteria. A benchmark of different scores measuring the relevance of a set of features was performed to decide which score to optimize during the band selection process. Band width optimization was then studied: the proposed method consisted in building a hierarchy of bands merged according to their similarities. Band selection was then processed within this hierarchy. The second part of the work consisted in the application of these spectral optimization algorithms to the case study of urban materials. A collection of urban materials spectra was first caught and from various spectral libraries ( ASTER , MEMORIES...). Spectral optimization was then performed on this dataset. A limited number (about 10) of well chosen bands appeared to be sufficient to classify next common materials (slates - asphalt - cement - gravel - metal - cobblestones - shingle - earth – tiles). Bands from short wave infrared spectral domain (1400 - 2500 nm) were shown again to be very useful to discriminate urban materials. However, quantitative results assessing the confusions between the materials must be considered carefully since some materials are very uncommon in the library of collected spectra, and thus their possible variability is not completely consideredCe travail s'inscrit dans la perspective de l'enrichissement des bases de données d'occupation du sol. La description de l'occupation du sol permet de produire des indicateurs environnementaux pour la gestion des écosystèmes et des territoires, en réponse à des besoins sociétaux, réglementaires et scientifiques. Aussi, des bases de données décrivant l'occupation du sol existent à différents niveaux (local, national, européen) ou sont en cours de constitution. Il est toutefois apparu que la connaissance de l'occupation du sol nécessaire pour certaines applications de modélisation de la ville (simulateurs de micro-météorologie, d'hydrologie, ou de suivi de pollutions), voire de suivi réglementaire (imperméabilisation des sols) est plus fine (au niveau sémantique et géométrique) que ce que contiennent ces bases de données. Des cartes de matériaux sont donc nécessaires pour certaines applications. Elles pourraient constituer une couche supplémentaire, à la fois dans des bases de données sur l'occupation du sol (comme l'occupation du sol à grande échelle de l'IGN) et dans des maquettes urbaines 3D.Aucune base de données existante ne contenant cette information, la télédétection apparaît comme la seule solution pour la produire. Néanmoins, du fait de la forte hétérogénéité des matériaux, de leur variabilité, mais aussi des fortes ressemblances entre classes distinctes, il apparaît que les capteurs optiques multispectraux classiques (limités aux 4 canaux rouge - vert - bleu - proche infrarouge) sont insuffisants pour bien discriminer des matériaux. Un capteur dit superspectral, c'est-à-dire plus riche spectralement, pourrait apporter une solution à cette limite. Ce travail s'est donc positionné dans l'optique de la conception d'un tel capteur et a consisté à identifier la meilleure configuration spectrale pour la classification des matériaux urbains, ou du moins à proposer des solutions s'en approchant. Un travail d'optimisation spectrale a donc été réalisé afin d'optimiser à la fois la position des bandes dans le spectre ainsi que leur largeur. Le travail s'est déroulé en deux temps. Une première tâche a consisté à définir et préciser les méthodes d'optimisation de bandes, et à les valider sur des jeux de données de référence de la littérature. Deux heuristiques d'optimisation classiques (l'une incrémentale, l'autre stochastique) ont été choisies du fait de leur généricité et de leur flexibilité, et donc de leur capacité à être utilisées pour différents critères de sélection d'attributs. Une comparaison de différentes mesures de la pertinence d'un jeu de bandes a été effectuée afin de définir le score à optimiser lors du processus de sélection de bandes. L'optimisation de la largeur des bandes a ensuite été étudiée : la méthode proposée consiste à préalablement construire une hiérarchie de bandes fusionnées en fonction de leur similarité, le processus de sélection de bandes se déroulant ensuite au sein de cette hiérarchie. La seconde partie du travail a consisté en l'application de ces algorithmes d'optimisation spectrale au cas d'étude des matériaux urbains. Une collection de spectres de matériaux urbains a d'abord été réunie à partir de différentes librairies spectrales (ASTER, MEMOIRES, ...). L'optimisation spectrale a ensuite été menée à partir de ce jeu de données. Il est apparu qu'un nombre limité de bandes bien choisies suffisait pour discriminer 9 classes de matériaux communs (ardoise - asphalte - ciment - gravier - métal - pavés en pierre - shingle - terre – tuile). L'apport de bandes issues du domaine de l'infrarouge onde courte (1400 - 2500 nm) pour la discrimination des matériaux a également été vérifiée. La portée des résultats chiffrés obtenus en terme de confusions entre les matériaux reste toutefois à nuancer du fait de la très faible représentation de certains matériaux dans la librairie de spectres collectés, ne couvrant donc pas la totalité de leur variabilit

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants

Hyperspectral imaging (HSI) has emerged as a transformative technology in imaging, characterized by its ability to capture a wide spectrum of light, including wavelengths beyond the visible range. This approach significantly differs from traditional imaging methods such as RGB imaging, which uses three color channels, and multispectral imaging, which captures several discrete spectral bands. Through this approach, HSI offers detailed spectral signatures for each pixel, facilitating a more nuanced analysis of the imaged subjects. This capability is particularly beneficial in applications like agricultural practices, where it can detect changes in physiological and structural characteristics of crops. Moreover, the ability of HSI to monitor these changes over time is advantageous for observing how subjects respond to different environmental conditions or treatments. However, the high-dimensional nature of hyperspectral data presents challenges in data processing and feature extraction. Traditional machine learning algorithms often struggle to handle such complexity. This is where 3D Convolutional Neural Networks (CNNs) become valuable. Unlike 1D-CNNs, which extract features from spectral dimensions, and 2D-CNNs, which focus on spatial dimensions, 3D CNNs have the capability to process data across both spectral and spatial dimensions. This makes them adept at extracting complex features from hyperspectral data. In this thesis, we explored the potency of HSI combined with 3D-CNN in agriculture domain where plant health and vitality are paramount. To evaluate this, we subjected lettuce plants to varying stress levels to assess the performance of this method in classifying the stressed lettuce at the early stages of growth into their respective stress-level groups. For this study, we created a dataset comprising 88 hyperspectral image samples of stressed lettuce. Utilizing Bayesian optimization, we developed 350 distinct 3D-CNN models to assess the method. The top-performing model achieved a 75.00\% test accuracy. Additionally, we addressed the challenge of generating valid 3D-CNN models in the Keras Tuner library through meticulous hyperparameter configuration. Our investigation also extends to the role of individual channels and channel groups within the color and near-infrared spectrum in predicting results for each stress-level group. We observed that the red and green spectra have a higher influence on the prediction results. Furthermore, we conducted a comprehensive review of 3D-CNN-based classification techniques for diseased and defective crops using non-UAV-based hyperspectral images.MITACSMaster of Science in Applied Computer Scienc

A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa

The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level

An intelligent classification system for land use and land cover mapping using spaceborne remote sensing and GIS

The objectives of this study were to experiment with and extend current methods of Synthetic Aperture Rader (SAR) image classification, and to design and implement a prototype intelligent remote sensing image processing and classification system for land use and land cover mapping in wet season conditions in Bangladesh, which incorporates SAR images and other geodata. To meet these objectives, the problem of classifying the spaceborne SAR images, and integrating Geographic Information System (GIS) data and ground truth data was studied first. In this phase of the study, an extension to traditional techniques was made by applying a Self-Organizing feature Map (SOM) to include GIS data with the remote sensing data during image segmentation. The experimental results were compared with those of traditional statistical classifiers, such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance classifiers. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification (with respect to the period of inundation by regular flooding) was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers. It also achieved higher accuracies for more classes in comparison to the other classifiers. However, it was also observed that different classifiers produced better accuracy for different classes. Therefore, the investigation was extended to consider Multiple Classifier Combination (MCC) techniques, which is a recently emerging research area in pattern recognition. The study has tested some of these techniques to improve the classification accuracy by harnessing the goodness of the constituent classifiers. A Rule-based Contention Resolution method of combination was developed, which exhibited an improvement in the overall accuracy of about 2% in comparison to its best constituent (SOM) classifier. The next phase of the study involved the design of an architecture for an intelligent image processing and classification system (named ISRIPaC) that could integrate the extended methodologies mentioned above. Finally, the architecture was implemented in a prototype and its viability was evaluated using a set of real data. The originality of the ISRIPaC architecture lies in the realisation of the concept of a complete system that can intelligently cover all the steps of image processing classification and utilise standardised metadata in addition to a knowledge base in determining the appropriate methods and course of action for the given task. The implemented prototype of the ISRIPaC architecture is a federated system that integrates the CLIPS expert system shell, the IDRISI Kilimanjaro image processing and GIS software, and the domain experts' knowledge via a control agent written in Visual C++. It starts with data assessment and pre-processing and ends up with image classification and accuracy assessment. The system is designed to run automatically, where the user merely provides the initial information regarding the intended task and the source of available data. The system itself acquires necessary information about the data from metadata files in order to make decisions and perform tasks. The test and evaluation of the prototype demonstrates the viability of the proposed architecture and the possibility of extending the system to perform other image processing tasks and to use different sources of data. The system design presented in this study thus suggests some directions for the development of the next generation of remote sensing image processing and classification systems

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others