Bayesian gravitation based classification for hyperspectral images.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Spatial Analysis for Landscape Changes

Recent increasing trends of the occurrence of natural and anthropic processes have a strong impact on landscape modification, and there is a growing need for the implementation of effective instruments, tools, and approaches to understand and manage landscape changes. A great improvement in the availability of high-resolution DEMs, GIS tools, and algorithms of automatic extraction of landform features and change detections has favored an increase in the analysis of landscape changes, which became an essential instrument for the quantitative evaluation of landscape changes in many research fields. One of the most effective ways of investigating natural landscape changes is the geomorphological one, which benefits from recent advances in the development of digital elevation model (DEM) comparison software and algorithms, image change detection, and landscape evolution models. This Special Issue collects six papers concerning the application of traditional and innovative multidisciplinary methods in several application fields, such as geomorphology, urban and territorial systems, vegetation restoration, and soil science. The papers include multidisciplinary studies that highlight the usefulness of quantitative analyses of satellite images and UAV-based DEMs, the application of Landscape Evolution Models (LEMs) and automatic landform classification algorithms to solve multidisciplinary issues of landscape changes. A review article is also presented, dealing with the bibliometric analysis of the research topic

Achieving sustainable development goals coupling earth observation data with machine learning

Tese de Doutoramento em Engenharia e Gestão Industrial, Universidade Lusíada, Vila Nova de Famalicão, 2021Exame público realizado em 09 de Junho de 2022The main purpose of this work is to assess and understand the achievement of Sustainable Development Goals by means of Earth Observation (EO) data and Machine Learning (ML) technologies. Thus, this study intends to promote and suggest the use of EO and ML in benefits to the Sustainable Development Goals (SDGs) to support and optimize the actual industry and field processes and moreover provide new insights (techniques) on EO approaches and applicability as well as ML techniques. A review on the Sustainable Development concept and its goals is presented followed by EO data and methods and its approaches relevant to this field, giving special attention to the contribution of ML methods and algorithms as well as their potential and capabilities to support the achievement of SDGs. Additionally, different ML approaches and techniques are reviewed (i.e., Classification and Regression techniques, Non-Binary Decision Tree (NBDT), and two novel methods are proposed, designated as: Random Forest built based on the Non-Binary Decision Tree (NBRF) and Fusion of techniques). Both developed methods are applied, optimized and validated to two case studies also aligned with specific SGDs: Case study I – Identification and mapping of healthy or infected crops, tackling SDGs 2, 8, 9 and 12; and Case study II - Deep-sea mining exploitation SDGs 8, 9, 12 and 14). Such is achieved by using data provided by European satellites or programs that allows to also contribute to the goals for Europe’s Space strategy. For Case study I, the parameters analysed to achieve the respective SDGs correspond to: several vegetation indices as well as the values of the spectral bands. Such parameters have been extracted by means of EO data (from Sentinel-2) and validated with different ML approaches. The results obtained from the different ML approaches suggest that for Case study I, the best classification technique (overall accuracy of 92.87%) as well as the best regression (Root mean square error of 0.148) corresponds to the Fusion of techniques All the applied techniques, however, show their applicability on this case study with good results, disregarding the NBDT which is the “weakest” one (best result on all tests: accuracy of 57.07%). For Case study II, the parameters analysed to achieve the respective SDGs correspond to the topography of the seafloor and, physical and biogeochemical ocean’s parameters. Such parameters have been extracted by means of EO data (from CMEMS and GEBCO) and validated with different ML approaches. The results of these approaches suggests that the best technique corresponds to the Fusion of techniques with a root mean square error of 0.196. However, not all the techniques proved to be appropriated, where the NBDT present the worst results (best result on all tests: accuracy 60.62%). Overall, it is observed that EO plays a key role in the monitoring and achievement of the SDGs given its cost-effectiveness pertaining to data acquisition on all scales and information richness, and the success of ML upon EO data analysis. The applicability of ML techniques allied to EO data has proven, by both case studies, that can contribute to the SDGs and can be extrapolated to other applications and fields. Keywords: Sustainable Development Goals; Earth Observation; Europe Space Strategy; Machine Learning; Deep-sea Mining; Agriculture

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments