Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods by formulating them in an adequate high-dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms.Publicad

Signal-to-noise ratio in reproducing kernel Hilbert spaces

This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful signal-to-noise regularizer for feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed independent. We give computationally efficient alternatives based on reduced-rank Nyström and projection on random Fourier features approximations, and analyze the bounds of performance and its stability. We illustrate the method through different examples, including nonlinear regression, nonlinear classification in channel equalization, nonlinear feature extraction from high-dimensional spectral satellite images, and bivariate causal inference. Experimental results show that the proposed kSNR yields more accurate solutions and extracts more noise-free features when compared to standard approaches

Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

Virtual visits as an alternative approach to learn urban and architectural heritage preservation during lockdown

Among the challenges faced at the university, during the lockdown from mid-March 2020, it was the need to give continuity to the learning of subjects that involve a direct approach to cultural property. In "Architectural History, Theory and Composition 3 - Rehabilitation", undergraduated students of the University of Seville’s Degree studies of Fundamentals in Architecture and teachers have practiced new ways of distance learning. Traditionally, reading and consulting materials are provided in digital formats (catalogues and urban plans, publications on buildings, urban history, cartography). Complementary, the visits of immersion in the heritage reality are especially productive in academic terms. Buildings, spaces, and urban perceptions are identified, so that students develop ways of seeing and recognize their complexity. The social, cultural, artistic, archaeological, landscape dimension prepares preservation or rehabilitation practices. This includes a visit to a qualified sector of the historic centre of Seville. This experience was initially planned to be onsite but due to the pandemic became virtual. The possibilities offered by the digital resources resulted in deeper approaches to other features of cultural property that were not frequently repaired by Architecture students

Estimating Gross Primary Productivity in Crops with Satellite Data, Radiative Transfer Modeling and Machine Learning

Monitoring spatio-temporal changes in terrestrial gross primary productivity (GPP) of crops is key for estimating, understanding and predicting global carbon fluxes. Satellite remote sensing has been widely applied in the last decades to monitor agricultural resources, and the amount and quality of remote sensing data continuously increase. Since recently, and partly due the European Copernicus Programme, an unprecedented amount of open access data suitable for agriculture observations is now available. Benefiting from recent developments in satellite remote sensing technology, great advances in machine learning and advancements in our understanding of photosynthetic processes leading to increasingly complex and detailed photosynthesis models, we developed a hybrid approach to model GPP using satellite reflectance data by combining radiative transfer modeling and machine learning (ML). We have combined process-based model SCOPE with ML algorithms to estimate GPP of C3 crops using a variety of satellite data (Sentinel-2, Landsat and MODIS) and ancillary meteorological information. We link reflectance and meteorological data directly with crop GPP, bypassing the need of retrieving the set of input vegetation parameters needed to represent photosynthesis in an intermediate step, while still accounting for the complex processes of the original model. Several ML models, trained with the simulated data, were tested and validated using flux tower data. First, we tested our approach using Sentinel-2 data, which provide high frequency of observation, high spatial resolution of 20 m and multiple bands including red edge. Our final neural network model was able to estimate GPP at the tested flux towers with r2 of 0.92 and RMSE of 1.38 gC m$^{-2}$ d-1. Our model successfully estimated GPP across a variety of C3 crop types and environmental conditions, including periods of no vegetation, even tough it did not use any additional local information from the site. Since our learning approach is fast and efficient in the test phase and, at the same time, is based on a process-based model (and not on local empirical relationships), it can be applied globally. Furthermore, the simulated training dataset can be easily adapted to band settings of different instruments, assuring thus consistency among many sensors. However, such a global application requires high computational power and therefore we applied our approach to Landsat and MODIS data using Google Earth Engine (GEE) platform that provides cloud computing resources for processing large geospatial datasets. The results were validated using the FLUXNET2015 Dataset

Herramientas y recursos de motivación online para actividades en clase

[EN] One essential condition for a good learning process by students is their motivation when facing the activities proposed by teachers in class. New generations of students, already formed by digital natives, push us to face changes in this teaching-learning process. We present a series of online tools that have allowed us to develop different activities such as interactive presentations or collaborative quizzes, among others, and have been very positive for motivating our students in the classroom.[ES] Una de las condiciones esenciales para un buen proceso de aprendizaje por parte del alumnado es la motivación del mismo a la hora de afrontar las actividades propuestas por parte del profesor en clase. Las nuevas generaciones de alumnos, formadas ya por nativos digitales, nos empujan a afrontar cambios en este proceso de enseñanza-aprendizaje. Presentamos una serie de herramientas on-line que nos han permitido la realización de diversas actividades tales como presentaciones interactivas o cuestionarios colaborativos, entre otras, que han resultado ser muy positivas a la hora de motivar al alumno en el aula.Este trabajo ha sido realizado en el marco del proyecto docente UV-SFPIE PID-1640839: “Docencia y evaluación a distancia: uso de herramientas propias de la UV y externas para mejorar la metodología docente en línea e híbrida en el área de ciencias”Adsuara, JE.; Fernández-Morán, R.; Gómez-Chova, L.; Laparra, V.; Ruescas Orient, AB.; Fernández-Torres, M.; Girbés-Juan, V.... (2022). Herramientas y recursos de motivación online para actividades en clase. Editorial Universitat Politècnica de València. 1055-1065. https://doi.org/10.4995/INRED2022.2022.158851055106