Deep Learning Based Classification Techniques for Hyperspectral Images in Real Time

Remote sensing can be defined as the acquisition of information from a given scene without coming into physical contact with it, through the use of sensors, mainly located on aerial platforms, which capture information in different ranges of the electromagnetic spectrum. The objective of this thesis is the development of efficient schemes, based on the use of deep learning neural networks, for the classification of remotely sensed multi and hyperspectral land cover images. Efficient schemes are those that are capable of obtaining good results in terms of classification accuracy and that can be computed in a reasonable amount of time depending on the task performed. Regarding computational platforms, multicore architectures and Graphics Processing Units (GPUs) will be considered

Clasifcación de imágenes de satélite de alta dimensionalidad

Traballo Fín de Grao en Enxeñaría Informática. Curso 2013-2014En 1972 la NASA(National Aeronautics and Space Administration) ponía en órbita el Landsat 1, originalmente llamado "Earth Resources Technology Satellite 1", comenzando así la era de la Teledetección. (En 2013, se lanzaba el Landsat 8). Existen diversos campos de aplicación de las imágenes hiperespectrales correspondientes a teledetección como puede ser el análisis de la cobertura terrestre, con el objetivo de clasificar las distintas zonas de la imagen, o el estudio de los océanos para poder encontrar las relaciones entre la profundidad del agua, el tipo de fondo y otras propiedades ópticas del agua. La clasicación consiste en la asignación de una determinada etiqueta de clase (tierra, agua, tejado, ...) a cada píxel, y constituye uno de los campos más activos de la teledetección. Las técnicas de teledetección se enfrentan a varios problemas debidos a la escasez de muestras etiquetadas y a la alta dimensionalidad de las imágenes, lo que dificulta el proceso de clasicación. Debido al gran avance que se está llevando a cabo en el campo de los sensores, cada vez disponemos de más datos por imagen, lo que obliga a mejorar las técnicas de clasicación de las imágenes hiperespectrales, imágenes en las que se dispone de cientos de valores de reflectancia por cada píxel. Por otra parte, y a pesar del avance de los sensores, la resolución de los mismos no es lo su ficientemente alta como para que en un único píxel se encuentre presente solamente un material. Con este trabajo se pretende desarrollar una aplicaci ón en el cual se puedan analizar las caracter ísticas de las im ágenes hiperespectrales, aplicar distintas técnicas de clasicación de imágenes multidimensionales y comparar los resultados de las distintas configuraciones.Utilizando la metodología Scrum desarrollaremos una aplicación fácil, intuitiva y útil que permita al usuario trabajar con imágenes hiperespectrales y configurar distintos escenarios con el fin de obtener una buena clasicació

TCANet for Domain Adaptation of Hyperspectral Images

The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniquesThis work was supported in part by Consellería de Educación, Universidade e Formación Profesional [grant numbers GRC2014/008, ED431C 2018/19, and ED431G/08] and Ministerio de Economía y Empresa, GovernmentofSpain[grantnumberTIN2016-76373-P].Allareco–fundedbytheEuropeanRegionalDevelopment Fund (ERDF). This work received financial support from the Xunta de Galicia and the European Union (European Social Fund - ESF)S

A hybrid CUDA, OpenMP, and MPI parallel TCA-based domain adaptation for classification of very high-resolution remote sensing images

Domain Adaptation (DA) is a technique that aims at extracting information from a labeled remote sensing image to allow classifying a different image obtained by the same sensor but at a different geographical location. This is a very complex problem from the computational point of view, specially due to the very high-resolution of multispectral images. TCANet is a deep learning neural network for DA classification problems that has been proven as very accurate for solving them. TCANet consists of several stages based on the application of convolutional filters obtained through Transfer Component Analysis (TCA) computed over the input images. It does not require backpropagation training, in contrast to the usual CNN-based networks, as the convolutional filters are directly computed based on the TCA transform applied over the training samples. In this paper, a hybrid parallel TCA-based domain adaptation technique for solving the classification of very high-resolution multispectral images is presented. It is designed for efficient execution on a multi-node computer by using Message Passing Interface (MPI), exploiting the available Graphical Processing Units (GPUs), and making efficient use of each multicore node by using Open Multi-Processing (OpenMP). As a result, an accurate DA technique from the point of view of classification and with high speedup values over the sequential version is obtained, increasing the applicability of the technique to real problemsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported in part by the Ministerio de Ciencia e Innovación, Government of Spain (grant numbers PID2019-104834GB-I00 and TED2021-130367B-I00), the Consellería de Educación, Universidade e Formación Profesional (grant number 2019–2022 ED431G-2019/04 and 2021–2024 ED431C 2022/16), and by the Junta de Castilla y León (project VA226P20 (PROPHET II Project)). All are co-funded by the European Regional Development Fund (ERDF)S

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precisionThis work was supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia and developed in partnership with the Babcock company to promote the use of unmanned technologies in civil services. We also have to acknowledge the support by the Ministerio de Ciencia e Innovación, Government of Spain (grant number PID2019-104834GB-I00), and Consellería de Educación, Universidade e Formación Profesional (grant number ED431C 2018/19, and accreditation 2019–2022 ED431G-2019/04). All are co-funded by the European Regional Development Fund (ERDF)S

HypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing

In this article, we present a desktop application for the analysis, reference data generation, registration, and supervised spatial-spectral classification of hyperspectral remote-sensing images through a simple and intuitive interface. Regarding the classification ability, the different classification schemes are implemented by using a chain structure as a base. It consists of five configurable stages that must be executed in a fixed order: preprocessing, spatial processing, pixel-wise classification, combination, and post-processing. The modular implementation makes its extension easy by adding new algorithms for each stage or new classification chains. The tool has been designed as a platform that is open to the incorporation of algorithms by the users interested in comparing classification schemes. As an example of use, a classification scheme based on the Quick Shift (QS) algorithm for segmentation and on Extreme Learning Machines (ELMs) or Support Vector Machines (SVMs) for classification is also proposed. The application is license-free, runs on the Linux operating system, and was developed in C language using the GTK library, as well as other free libraries to build the graphical user interfaces (GUIs)This work was supported by the Xunta de Galicia, Programme for Consolidation of Competitive Research Groups [2014/008]; Ministry of Science and Innovation, Government of Spain, cofounded by the FEDER funds of European Union [TIN2013-41129-P]S

Texture-based analysis of hydrographical basins with multispectral imagery

In this paper the problem of studying the presence of different vegetation species and artificial structures in the riversides by using multispectral remote sensing information is studied. The information provided contributes to control the water resources in a region in northern Spain called Galicia. The problem is solved as a supervised classification computed over five-band multispectral images obtained by an Unmanned Aerial Vehicle (UAV). A classification scheme based on the extraction of spatial, spectral and textural features previous to a hierarchical classification by Support Vector Machine (SVM) is proposed. The scheme extracts the spatial-spectral information by means of a segmentation algorithm based on superpixels and by computing morphological operations over the bands of the image in order to generate an Extended Morphological Profile (EMP). The texture features extracted help in the classification of vegetation classes as the spatial-spectral features for these classes are not discriminant enough. The classification is computed over segments instead of pixels, thus reducing the computational cost. The experimental results over four real multispectral datasets from Galician riversides show that the proposed scheme improves over a standard classification method achieving very high accuracy resultsThis work was supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia and developed in partnership with the Babcock company to promote the use of unmanned technologies in civil services. We also have to acknowledge the support by the Consellería de Educación, Universidade e Formación Profesional [grant numbers GRC2014/008, ED431C 2018/19, and ED431G/08], Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y León - ERDF (PROPHET Project) [grant number VA082P17]. All are co–funded by the European Regional Development Fund (ERDF)S