Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode

Integrating Remote Sensing and Geographic Information Systems

Remote sensing and geographic information systems (GIS) comprise the two major components of geographic information science (GISci), an overarching field of endeavor that also encompasses global positioning systems (GPS) technology, geodesy and traditional cartography (Goodchild 1992, Estes and Star 1993, Hepner et al. 2005). Although remote sensing and GIS developed quasi-independently, the synergism between them has become increasingly apparent (Aronoff 2005). Today, GIS software almost always includes tools for display and analysis of images, and image processing software commonly contains options for analyzing ‘ancillary’ geospatial data (Faust 1998). The significant progress made in ‘integration’ of remote sensing and GIS has been well-summarized in several reviews (Ehlers 1990, Mace 1991, Hinton 1996, Wilkinson 1996). Nevertheless, advances are so rapid that periodic reassessment of the state-of-the-art is clearly warranted

SIRENE: A Spatial Data Infrastructure to Enhance Communities' Resilience to Disaster-Related Emergency

Abstract Planning in advance to prepare for and respond to a natural hazard-induced disaster-related emergency is a key action that allows decision makers to mitigate unexpected impacts and potential damage. To further this aim, a collaborative, modular, and information and communications technology-based Spatial Data Infrastructure (SDI) called SIRENE—Sistema Informativo per la Preparazione e la Risposta alle Emergenze (Information System for Emergency Preparedness and Response) is designed and implemented to access and share, over the Internet, relevant multisource and distributed geospatial data to support decision makers in reducing disaster risks. SIRENE flexibly searches and retrieves strategic information from local and/or remote repositories to cope with different emergency phases. The system collects, queries, and analyzes geographic information provided voluntarily by observers directly in the field (volunteered geographic information (VGI) reports) to identify potentially critical environmental conditions. SIRENE can visualize and cross-validate institutional and research-based data against VGI reports, as well as provide disaster managers with a decision support system able to suggest the mode and timing of intervention, before and in the aftermath of different types of emergencies, on the basis of the available information and in agreement with the laws in force at the national and regional levels. Testing installations of SIRENE have been deployed in 18 hilly or mountain municipalities (12 located in the Italian Central Alps of northern Italy, and six in the Umbria region of central Italy), which have been affected by natural hazard-induced disasters over the past years (landslides, debris flows, floods, and wildfire) and experienced significant social and economic losses

Real-time event-based unsupervised feature consolidation and tracking for space situational awareness

Earth orbit is a limited natural resource that hosts a vast range of vital space-based systems that support the international community's national, commercial and defence interests. This resource is rapidly becoming depleted with over-crowding in high demand orbital slots and a growing presence of space debris. We propose the Fast Iterative Extraction of Salient targets for Tracking Asynchronously (FIESTA) algorithm as a robust, real-time and reactive approach to optical Space Situational Awareness (SSA) using Event-Based Cameras (EBCs) to detect, localize, and track Resident Space Objects (RSOs) accurately and timely. We address the challenges of the asynchronous nature and high temporal resolution output of the EBC accurately, unsupervised and with few tune-able parameters using concepts established in the neuromorphic and conventional tracking literature. We show this algorithm is capable of highly accurate in-frame RSO velocity estimation and average sub-pixel localization in a simulated test environment to distinguish the capabilities of the EBC and optical setup from the proposed tracking system. This work is a fundamental step toward accurate end-to-end real-time optical event-based SSA, and developing the foundation for robust closed-form tracking evaluated using standardized tracking metrics

Predicting vegetation characteristics in a changing environment by means of laser scanning

Accurate and up-to-date information concerning vegetation characteristics is needed for decision-making from individual-tree-level management activities to the strategic planning of forest resources. Outdated information may lead to unbeneficial or even wrong decisions, at least when it comes to the timing of management activities. Airborne laser scanning (ALS) has so far been successfully used for applications involving detailed vegetation mapping because of its capability to simultaneously produce accurate information on vegetation and ground surfaces. The aim of this dissertation was to develop methods for characterizing vegetation and its changes in varying environments. A method called multisource single-tree inventory (MS-STI) was developed in substudy I to update urban tree attributes. In MS-STI stem map was produced with terrestrial laser scanning (TLS) and by combining the stem map with predictors derived from ALS data it was possible to obtain improved estimates of diameter-at-breast height but also to produce new attributes such as height and crown size. Boat-based mobile laser scanning (MLS) data were employed in substudy II to map riverbank vegetation and identify changes. The overall classification accuracy of 73% was obtained, which is similar to accuracies found in other studies. With multi-temporal MLS data sets changes in vegetation were mapped year to year. In substudy III, open access ALS data were combined with multisource national forest inventory (NFI) data to investigate the drivers associated to wind damage. The special interest was in ALS-based predictors to map areas with wind disturbance and apply logistic regression to produce a continuous probability surface of wind predisposition to identify areas most likely to experience wind damage. The results demonstrated that a combination of ALS and multisource NFI in the modelling approach increased the prediction accuracy from 76% to 81%. The dissertation showed the capability of ALS and MLS for characterizing vegetation and mapping changes in varying environments. The developed applications could increase and expand the utilization of multi-temporal 3D data sets as well as increase data value. The results of this dissertation can be utilized in producing more accurate, diverse, and up-to-date information for decision-making related to natural resources.Luonnonvaroja koskevaa päätöksentekoa varten tarvitaan luotettavaa ja ajantasaista tietoa, oli kyse sitten yksittäiseen puuhun liittyvistä toimenpiteistä tai laajojen alueiden strategisesta suunnittelusta. Vanhentunut tieto voi johtaa epäedullisiin tai jopa vääriin ratkaisuihin, erityisesti hoitotoimenpiteiden ajoituksen osalta. Ilmalaserkeilaus on menetelmä, jossa yksityiskohtaista kolmiulotteista tietoa tuotetaan esimerkiksi lentokoneeseen tai helikopteriin asennetun laserkeilaimen avulla. Laserkeilan mittaa etäisyyttä kohteeseen laserpulssin kulkuajan perusteella. Ilmalaserkeilaus on jo operatiivisessa käytössä metsävaratiedonkeruussa Pohjoismaissa sillä sen avulla voidaan tuottaa tarkkaa tietoa samanaikaisesti sekä maanpinnan korkeudesta ja maaston muodoista että kasvillisuuden pituudesta ja tiheydestä. Maastolaserkeilauksella tarkoitetaan pienemmän alueen inventointiin soveltuvaa menetelmää, jossa laserkeilain on kolmijalan päällä tai liikkuvalla alustalla. Väitöskirjan tavoitteena oli kehittää menetelmiä kasvillisuuden ominaisuuksien ennustamiseen laserkeilauksen avulla erilaisissa ympäristöissä. Väitöskirja koostuu kolmesta osajulkaisusta, joista ensimmäisessä kehitettiin monilähteinen yksittäisten puiden inventointimenetelmä kaupunkipuiden tunnusten päivittämiseen. Kyseisessä menetelmässä maastolaserkeilauksen avulla tuotettiin puukartta, joka yhdistettiin ilmalaserkeilauksella saatuihin tietoihin. Ilmalaserkeilauksesta saatujen yksittäisten puiden latvojen pituus- ja tiheystunnusten avulla voitiin parantaa kaupunkipuiden läpimittatietoja sekä tuottaa uusia tunnuksia kuten pituus ja latvuksen koko lisättäväksi kaupunkipuurekisterin tietokantaan. Toisessa osajulkaisussa käytettiin veneeseen asennettua laserkeilainta jokiympäristön kasvillisuuden kartoittamiseen sekä kasvillisuudessa tapahtuneiden muutosten havainnoimiseen. Kasvillisuus ja paljas maa oli mahdollista erotella 73 prosentin tarkkuudella, vastaaviin tarkkuuksiin on päästy myös aiemmissa tutkimuksissa, joissa tosin hyödynnettiin tarkempaa maastoaineistoa. Useampiaikaisilla aineistoilla oli mahdollista kartoittaa vuosien välillä tapahtuneita kasvillisuuden muutoksia. Kolmannessa osajulkaisussa hyödynnettiin avoimesti saatavilla olevaa ilmalaserkeilaus- ja monilähteistä valtion metsien inventoinnin (VMI) aineistoa tuulituhojen kartoittamiseen sekä ennustamiseen. Osajulkaisussa ennustettiin tuulituhoriskin suuruutta ilmalaserkeilauksesta saatavien maanpinnan korkeuden ja kasvillisuuden pituuden sekä monilähde-VMI-aineistosta saadun puulajitiedon avulla. Tarkoituksena oli selvittää tuhoriskille erityisen alttiit alueet mahdollisia metsänhoitotoimenpiteitä varten. Puulajitieto lisäsi tuulituhojen kartoitustarkkuutta 76 prosentista 81 prosenttiin. Väitöskirja esitteli erilaisilta alustoilta tehtävän laserkeilauksen kykyä kasvillisuuden luonnehtimiseen sekä muutosten huomioimiseen erilaisissa ympäristöissä monipuolista päätöksentekoa varten. Kaupunkiympäristöissä yksittäisten puiden tunnukset ovat kohdennettujen toimenpiteiden kannalta tärkeitä, kun taas tietoa jokiympäristöjen kasvillisuudesta ja sen muutoksista voidaan hyödyntää päivitettäessä tulvariskimalleja. Tieto tuulituhoille riskialttiista alueista voi auttaa metsänomistajia ja ammattilaisia metsänhoitotoimenpiteiden suunnittelussa. Väitöskirjassa kehitettyjen menetelmien avulla voidaan laajentaa useampiaikaisten laserkeilausaineistojen hyödyntämistä sekä saada lisäarvoa aineistoista. Väitöskirjan tuloksia voidaan hyödyntää tarkemman, monipuolisemman ja ajantasaisemman tiedon tuottamisessa erilaisessa luonnonvaroja koskevassa suunnittelussa ja päätöksenteossa

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution

The potential of multi-sensor satellite data for applications in environmental monitoring with special emphasis on land cover mapping, desertification monitoring and fire detection

Unprecedented pressure on the physical, chemical and biological systems of the Earth results in environment problems locally and globally, therefore the detection and understanding of environmental change based on long-term environmental data is very urgent. In developing countries/regions, because the natural resources are depleted for development while environmental awareness is poor, environment is changing faster. The insufficient environmental investment and sometimes infeasible ground access make the environment information acquisition and update inflexible through standard methods. With the main advantages of global observation, repetitive coverage, multispectral sensing and low-cost implementation, satellite remote sensing technology is a promising tool for monitoring environment, especially in the less developed countries. Multi-sensor satellite images may provide increased interpretation capabilities and more reliable results since data with different characteristics are combined and can achieve improved accuracies, better temporal coverage, and better inference about the environment than could be achieved by the use of a single sensor alone. The objective of this thesis is to demonstrate the capability and technique of the multi-sensor satellite data to monitor the environment in developing countries. Land cover assessment of Salonga national park in the democratic republic of Congo of Africa, desertification monitoring in North China and tropical/boreal wildland fire detection in Indonesia/Siberia were selected as three cases in this study for demonstrating the potential of multi-sensor application to environment monitoring. Chapter 2 demonstrates the combination of Landsat satellite images, Global Position System (GPS) signals, aerial videos and digital photos for assessing the land cover of Salonga national park in Congo. The purpose was to rapidly assess the current status of Salonga national park, especially its vegetation, and investigated the possible human impacts by shifting cultivation, logging and mining. Results show that the forests in the Salonga national park are in very good condition. Most of the area is covered by undisturbed, pristine evergreen lowland and swamp forests. No logging or mining activity could be detected. Chapter 3 demonstrates the one full year time series SPOT VEGETATION with coarse resolution of 1 km and the ASTER images with higher resolution of 15 meters as well as Landsat images for land cover mapping optimised for desertification monitoring in North-China. One point six million km2 were identified as risk areas of desertification. Results show within a satellite based multi-scale monitoring system SPOT VEGETATION imagery can be very useful to detect large scale dynamic environmental changes and desertification processes which then can be analysed in more detail by high resolution imagery and field surveys. Chapter 4 demonstrates the detection of tropical forest fire and boreal forest fire. Firstly, the ENVISAT ASAR backscatter dynamics and ENVISAT full resolution MERIS characteristics of fire scars were investigated in Siberian boreal forest, and results show these two sensors are very useful for fire monitoring and impact assessment. Secondly, the general capability and potential of ENVISAT multi-sensor of MERIS, AATSR, ASAR as well as NOAA-AVHRR and MODIS for tropical forest fire event monitoring and impact assessment in tropical Indonesia were investigated, and results show the capability of ENVISAT to acquire data from different sensors simultaneously or within a short period of time greatly enhances the possibilities to monitor fire occurrence and assess fire impact. Finally, the multi-sensor technology was applied to the disastrous boreal forest fire event of 2003 around East and West Lake Baikal in Siberia, and results show that 202,000 km2 burnt in 2003 within the study area of 1,300,000 km2, which is more than the total burnt area between 1996-2002. 71.4% of the burnt areas were forests, and 11.6% were wetlands or bogs. In total 32.2% of the forest cover has been burnt at least once from 1996 to 2003, 14% of the area has been affected at least twice by fire. These demonstrations show that in spite of the two disadvantages of indirect satellite measurements and the difficulty of detecting the cause of environment change, multi-sensor satellite technology is very useful in environment monitoring. However more studies on multi-sensor data fusion methods are needed for integrating the different satellite data from various sources. The lack of personnel skilled in remote sensing is a severe deficiency in developing countries, so the technology transfer from the developed countries is needed