Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Landscape scale mapping of tundra vegetation structure at ultra-high resolution using UAVs and computer vision

Ilmastomuutoksella on voimakkain vaikutus suurten leveysasteiden ekosysteemeissä, jotka ovat sopeutuneet viileään ilmastoon. Jotta suurella mittakaavalla havaittuja muutoksia tundrakasvillisuudessa ja niiden takaisinkytkentävaikutuksia ilmastoon voidaan ymmärtää ja ennustaa luotettavammin, on syytä tarkastella mitä tapahtuu pienellä mittakaavalla; jopa yksittäisissä kasveissa. Lähivuosikymmenten aikana tapahtunut teknologinen kehitys on mahdollistanut kustannustehokkaiden, kevyiden ja pienikokoisten miehittämättömien ilma-alusten (UAV) yleistymisen. Erittäin korkearesoluutioisten aineistojen (pikselikoko <10cm) lisääntyessä ja tullessa yhä helpommin saataville, ympäristön tarkastelussa käytetyt kaukokartoitusmenetelmät altistuvat paradigmanmuutokselle, kun konenäköön ja -oppimiseen perustuvat algoritmit ja analyysit yleistyvät. Menetelmien käyttöönotto on houkuttelevaa, koska ne mahdollistavat joustavan ja pitkälle automatisoidun aineistonkeruun ja erittäin tarkkojen kaukokartoitustuotteiden tuottamisen vaikeasti tavoitettavilta alueilta, kuten tundralla. Luotettavien tulosten saaminen vaatii kuitenkin huolellista suunnittelua sekä prosessointialgoritmien ja -parametrien pitkäjänteistä testaamista. Tässä tutkimuksessa tarkasteltiin, kuinka tarkasti tavallisella digitaalikameralla kerätyistä ilmakuvista johdetuilla muuttujilla voidaan kartoittaa kasvillisuuden rakennetta maisemamittakaavalla. Kilpisjärvellä Pohjois-Fennoskandiassa kerättiin dronella kolmensadan hehtaarin kokoiselta alueelta yhteensä noin 10 000 ilmakuvasta koostuva aineisto. Lisäksi alueella määritettiin 1183 pisteestä dominantti putkilokasvillisuus, sekä kasvillisuuden korkeus. Ilmakuvat prosessoitiin tiheiksi kolmiulotteisiksi pistepilviksi konenäköön ja fotogrammetriaan perustuvalla SfM (Structure from Motion) menetelmällä. Pistepilvien pohjalta interpoloitiin maastomalli sekä kasvillisuuden korkeusmalli. Lisäksi tuotettiin koko alueen kattava ilmakuvamosaiikki. Näiden aineistojen pohjalta laskettiin muuttujia, joita käytettiin yhdessä maastoreferenssiaineiston kanssa kasvillisuuden objektipohjaisessa analyysissä (GEOBIA, Geographical Object-Based Image Analysis). Suodatetut maanpintapisteet vastasivat luotettavasti todellista maanpinnan korkeutta koko alueella ja tuotetut korkeusmallit korreloivat voimakkaasti maastoreferenssiaineiston kanssa. Maastomallin virhe oli suurin alueilla, joilla oli korkeaa kasvillisuutta. Valaistusolosuhteissa ja kasvillisuudessa tapahtuneet muutokset ilmakuvien keruun aikana aiheuttivat haasteita objektipohjaisen analyysin molemmissa vaiheissa: segmentoinnissa ja luokittelussa. mutta kokonaistarkkuus parani 0,27:stä 0,,54:n kun luokitteluun lisättiin topografiaa, kasvillisuuden korkeutta ja tekstuuria kuvaavia muuttujia ja kohdeluokkien lukumäärää vähennettiin. Konenäköön ja –oppimiseen perustuvat menetelmät pystyvät tuottamaan tärkeää tietoa tundran kasvillisuuden rakenteesta, erityisesti kasvillisuuden korkeudesta, maisemassa. Lisää tutkimusta kuitenkin tarvitaan parhaiden algoritmien ja parametrien määrittämiseksi tundraympäristössä, jossa ympäristöolosuhteet muuttuvat nopeasti ja kasvillisuus on heterogeenistä ja sekoittunutta, mikä aiheuttaa eroja ilmakuvien välillä ja lisää vaikeuksia analyyseissä.Climate change has the strongest impact on high-latitude ecosystems that are adapted to cool climates. In order to better understand and predict the changes in tundra vegetation observed on large scales as well as their feedbacks onto climate, it is necessary to look at what is happening at finer scales; even in individual plants. Technological developments over the past few decades have enabled the spread of cost-effective, light and small unmanned aerial vehicles (UAVs). As very high-resolution data (pixel size <10cm) becomes more and more available, the remote sensing methods used in environmental analysis become subject to a paradigm shift as algorithms and analyzes based on machine vision and learning turn out to be more common. Harnessing new methods is attractive because they allow flexible and highly automated data collection and the production of highly accurate remote sensing products from hard-to-reach areas such as the tundra. However, obtaining reliable results requires careful planning and testing of processing algorithms and parameters. This study looked at how accurately variables derived from aerial images collected with an off-the-shelf digital camera can map the vegetation structure on a landscape scale. In Kilpisjärvi, northern Fennoscandia, a total of ~ 10,000 aerial photographs were collected by drone covering an area of three hundred hectares. In addition, dominant vascular plants were identified from 1183 points in the area, as well as vegetation height. Aerial images were processed into dense three-dimensional point clouds by using SfM (Structure from Motion) method, which is based on computer vision and digital photogrammetry. From the point clouds terrain models and vegetation height models were interpolated. In addition, image mosaic covering the entire area was produced. Based on these data, predictive variables were calculated, which were used together with the terrain reference data in Geographical Object-Based Image Analysis (GEOBIA). The filtered ground points corresponded to observations throughout the region, and the produced elevation models strongly correlated with the ground reference data. The terrain model error was greatest in areas with tall vegetation. Changes in lighting conditions and vegetation during aerial image surveys posed challenges in both phases of object-based analysis: segmentation and classification. but overall accuracy improved from 0.27 to 0.54 when topography, vegetation height and texture variables were added to the classifier and the number of target classes was reduced. Methods based on machine vision and learning can produce important information about vegetation structure, vegetation height, in a landscape. However, more research is needed to determine the best algorithms and parameters in a tundra environment where environmental conditions change rapidly and vegetation is heterogeneous and mixed, causing differences between aerial images and difficulties in analyses

Analysis and design of multifunctional agricultural landscapes : a graph theoretic approach

This thesis deals with the development of quantitative methodologies for the evaluation of landscape functions and their interactions in multifunctional agricultural landscapes. It focuses on the spatial coherence of hedgerow networks for ecological functions and landscape character for perception of landscape identity, and on their integration in a multifunctional and multiscale trade-off analysis. Graph theory provided the basis for new methodologies that are applied in this research

Recreation, tourism and nature in a changing world : proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 2010

Proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 201

Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia

AbstractRecent neuroanatomical pattern classification studies have attempted to individually classify cases with psychotic disorders using morphometric MRI data in an automated fashion. However, this approach has not been tested in population-based samples, in which variable patterns of comorbidity and disease course are typically found. We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited period of time, in comparison with next-door healthy controls. Sixty-two cases of first-episode schizophrenia or schizophreniform disorder and 62 age, gender and educationally-matched controls underwent 1.5T MRI scanning at baseline, and were naturalistically followed-up over 1year. T1-weighted images were used to train a high-dimensional multivariate classifier, and to generate both spatial maps of the discriminative morphological patterns between groups and ROC curves. The spatial map discriminating first-episode schizophrenia patients from healthy controls revealed a complex pattern of regional volumetric abnormalities in the former group, affecting fronto-temporal-occipital gray and white matter regions bilaterally, including the inferior fronto-occipital fasciculus, as well as the third and lateral ventricles. However, an overall modest DA (73.4%) was observed for the individual discrimination between first-episode schizophrenia patients and controls, and the classifier failed to predict 1-year prognosis (remitting versus non-remitting course) of first-episode schizophrenia (DA=58.3%). In conclusion, using a “real world” sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia subjects and next-door healthy controls, and poor discriminative power to predict the 1-year prognosis of first-episode schizophrenia

An Intelligent Hybrid Optimization with Deep Learning model-based Schizophrenia Identification from Structural MRI

One of the fatal diseases that claim women while they are pregnant or nursing is schizophrenia. Despite several developments and symptoms, it can be challenging to discern between benign and malignant conditions. The main and most popular imaging method to predict Schizophrenia is MR Images. Furthermore, a few earlier models had a definite accuracy when diagnosing the condition. Stable MRI criteria must also be implemented immediately. Compared to other imaging technologies, the MRI imaging method is the simplest, safest, and most common for predicting Schizophrenia. The following factors are mostly involved in the subprocess for the initial MRI image. Before calculating the length between the sample point and the cluster center, the initial cluster center of the sample is identified. Classification is done according to how far the sample point is from the cluster center. The picture is then generated once the new cluster center has been derived using the classification history and verified to match the cluster convergence condition. A grey wolf optimization-based convolutional neural network approach is offered to get beyond the limitations and find schizophrenia, whether its hazardous or not. Many MRI images or datasets are analyzed in a short time, and the results show a more accurate or higher rate of schizophrenia recognition