Study of Digital Image Processing Techniques in Remote Sensing

Digital image processing (DIP) has been an essential explanation behind the rise of remote detecting as a viable methods for regular assets appraisal. Expanding intricacy and diminishing expense of computerized equipment empower quick control of information for most extreme data extraction, which would be very troublesome if certainly feasible with established photographic translation. DIP is seen regarding a fundamental necessity, which is intelligent (close continuous) activity, and two general attributes, which are high volume of information and rehashed utilization of a grouping of calculations to every component of information. Computational and operational necessities for a scope of DIP capacities are checked on. These incorporate upgrades, changes, geometric and radiometric adjustments, encoding and characterization. The suggestions are talked about regarding equipment necessities. While a universally useful PC interfaced with a video show offers the least difficult DIP framework, it takes the pipeline and parallel models of showcase processors, and to a specific degree, exhibit processors to execute most DIP works in close constant, as requested by the necessity of interactive processing

Real world 3D accuracy achievable of Australian standard 5488-2013 classification of subsurface utility information using electromagnetic field detection

There are hundreds of kilometres of underground services - pipes and cables that carry vital services such as water, electricity, communications and gas are buried throughout Australia and that number is increasing every year. Damage to these vital services is not only costly it is also disruptive to the surrounding community; there is also the risk of personal injury and or death that could be caused by damaging underground infrastructure.(Dial Before You Dig, 2015. The importance of locating these vital utilities before construction to aid in avoiding them is well known. Currently the most readily used technology used to locate these services is electromagnetic field detection. This technology is used to pinpoint the service location to an X, Y and Z position. Can this technology meet the new Australia Standard - Classification of Subsurface Utility Information for positional accuracy in a real world test? To determine this, a test site was chosen that contains an underground line. After using a range of electromagnetic field detection equipment to locate the line, the true position will be revealed using non-destructive digging methods. The derived position of the line from different electromagnetic field detection equipment will be compared against the true position surveyed points. An error analysis will be provided showing a comparison of the methods and thus determine if they meet the quality specified in the Australian Standard

Application of Geospatial Techniques in Agricultural Resource Management

Although technological advancements have sparked the beginning of the fourth agricultural revolution, human beings are still facing severe problems such as shrinking croplands, dwindling water supplies, negative consequences of climate change, and so on in achieving agricultural resilience to meet the demands of the growing population over the globe. Geospatial techniques involving the integrated use of geographic information system (GIS), remote sensing (RS), and artificial intelligence (AI) provide a strong basis for sustainable management of agricultural resources aimed at increased agricultural production. In recent times, these advanced tools have been increasingly used in agricultural production at local, regional, and global levels. This chapter focuses on the widespread application of geospatial techniques for agricultural resource management by monitoring crop growth and yield forecasting, crop disease and pest infestation, land use and land cover mapping, flood monitoring, and water resource management. Moreover, we also discuss various methodologies involved in monitoring and mapping abovementioned agricultural resources. This chapter will provide deep insight into the available literature on the use of geospatial techniques in the monitoring and management of agricultural resources. Moreover, it will be helpful for scientists to develop integrated methodologies focused on exploring satellite data for sustainable management of agricultural resources

Radio Base Stations and Electromagnetic Fields: GIS Applications and Models for Identifying Possible Risk Factors and Areas Exposed. Some Exemplifications in Rome

This paper—which is contextualized in the discussion on the methodological pluralism and the main topics of medical geography, the complexity theory in geographies of health, the remaking of medical geography and ad hoc systems of data elaboration—focuses on radio base stations (RBSs) as sources of electromagnetic fields, to provide GIS applications and simplifying-prudential models that are able to identify areas that could potentially be exposed to hazard. After highlighting some specific aspects regarding RBSs and their characteristics and summarizing the results of a number of studies concerning the possible effects of electromagnetic fields on health, we have taken an area of north-east Rome with a high population and building density as a case study, and we have provided some methodological and applicative exemplifications for different situations and types of antennas. Through specific functionalities and criteria, drawing inspiration from a precautionary principle, these exemplifications show some particular cases in order to support: possible risk factor identification, surveillance and spatial analysis; correlation analysis between potential risk factors and outbreak of diseases and symptoms; measurement campaigns in heavily exposed areas and buildings; education policies and prevention actions. From an operative viewpoint, we have: conducted some field surveys and recorded data and images with specific geotechnological and geomatics instruments; retraced the routes by geobrowsers and basemaps and harmonized and joined up the materials in a GIS environment; used different functions to define, on aero-satellite images, concentric circular buffer zones starting from each RBS, and geographically and geometrically delimited the connected areas subject to high and different exposure levels; produced digital applications and tested prime three-dimensional models, in addition to a video from a bird’s eye view perspective, able to show the buildings in the different buffer zones and which are subject to a hazard hierarchy due to exposure to an RBS. A similar GIS-based model—reproposable with methodological adjustments to other polluting sources—can make it possible to conceive a dynamic and multiscale digital system functional in terms of strategic planning, decision-making and public health promotion in a performant digital health information system

Mineral exploration of rock wastes from sulfide mining using airborne hyperspectral imaging

Open-pit sulfide mining produces large quantities of waste rock that may contain materials of economic interest. The exposure of sulfides accumulation may also pose a hazard to the environment by causing phenomena such as acid mine drainage. This Master Thesis aims to map and provide a geological characterization of the rock wastes of Corta Atalaya open pit in Río Tinto, Spain. For this purpose, different hyperspectral imaging technologies that have already demonstrated their effectiveness in mineral detection such as airborne remote sensing in the VNIR and SWIR domain are used. This study is complemented with the incorporation of an innovative hyperspectral method, the airborne LWIR. Our approach makes use of a set of different spectral methods, and established image processing routines, such as band ratios, and minimum wavelength maps. Supervised classifications are also employed as a mean to extrapolate mapped rock types to larger unmapped areas, spectral angle maps, and to identify high abundances of endmember lithologies, spectral unmixing techniques. Furthermore, this study will lay the foundations and pave the way for possible future lines of research regarding the Corta Atalaya rock wastes.Universidad de Granada. Máster en Geofísica y Meteorología (GEOMET). Curso 2019-2020European Union’s Horizon 2020 research and innovation programme under grant agreement nº 77648

Εκτίμηση Εδαφικής Υγρασίας από Πολυφασματικά και Ραντάρ Δορυφορικά Δεδομένα με χρήση του Google Earth Engine και Τεχνικών Μηχανικής Μάθησης

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach.

The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet\u27s glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial. Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the other hand, deep-learning (DL) algorithms, which can learn from linear and non-linear data in a hierarchical way robust representations and discriminative features, have recently become a hotspot in the field of machine learning and have been implemented with success in the geospatial and remote sensing field. This study demonstrates that deep learning, particularly long-short memory autoencoder architecture (LSTM-AE) is capable of fully exploiting archives of passive microwave time series data. In this thesis, An LSTM-AE algorithm was used to reduce and capture essential relationships between attributes stored as brightness temperature within pixel time series and k-means clustering is applied to cluster the leaned representations. The final output map highlights the melt extent in Antarctica

Department of Defense Dictionary of Military and Associated Terms

The Joint Publication 1-02, Department of Defense Dictionary of Military and Associated Terms sets forth standard US military and associated terminology to encompass the joint activity of the Armed Forces of the United States. These military and associated terms, together with their definitions, constitute approved Department of Defense (DOD) terminology for general use by all DOD components

Remote sensing applications: an overview

Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-biophysical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given