Search results for "kaukokartoitus"

showing 9 items of 29 documents

Minimal learning machine in anomaly detection from hyperspectral images

2020

Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.

lcsh:Applied optics. PhotonicsComputer sciencehyperspectral imagingData needs0211 other engineering and technologies02 engineering and technologylcsh:TechnologyConstant false alarm rateremote sensing0202 electrical engineering electronic engineering information engineering021101 geological & geomatics engineeringData collectionlcsh:Tbusiness.industryspektrikuvausProcess (computing)lcsh:TA1501-1820Hyperspectral imagingPattern recognitionminimal learning machineDroneanomaly detectionkoneoppiminenMinimal learning machinelcsh:TA1-2040020201 artificial intelligence & image processingAnomaly detectionArtificial intelligencekaukokartoituslcsh:Engineering (General). Civil engineering (General)business
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Autonomous hyperspectral UAS photogrammetry for environmental monitoring applications

2014

Abstract. The unmanned airborne system (UAS) remote sensing using lightweight multi- and hyperspectral imaging sensors offer new possibilities for the environmental monitoring applications. Based on the accurate measurements of the way in which the object reflect and emit energy, wide range of affecting variables can be monitored. Condition for reliable applications is reliable and accurate input data. In many applications, installation of geometric and radiometric reference targets in the object area is challenging, for instance, in forest or water areas. On the other hand, UASs are often operated in very poor conditions, under clouds or under variable cloud cover. Our objective is to deve…

lcsh:Applied optics. PhotonicsgeometryPoint cloudradiometryphotogrammetrylcsh:Technologykalibrointiremote sensingEnvironmental monitoringRemote sensingBlock (data storage)fotogrammetriaData processingblockForest inventorylcsh:Tlcsh:TA1501-1820Hyperspectral imagingcalibrationGeographyPhotogrammetryhyperspectrallcsh:TA1-2040Precision agriculturekaukokartoitusgeometriaUASlcsh:Engineering (General). Civil engineering (General)point cloud
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Updating strategies for distance based classification model with recursive least squares

2022

Abstract. The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach ma…

luokitus (toiminta)Minimal Learning Machinemachine learningkoneoppiminenclassificationhyperspectral imagingkaukokartoitusRecursive Least Squaresreal-time computationhyperspektrikuvantaminen
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What makes segmentation good? A case study in boreal forest habitat mapping

2013

Segmentation goodness evaluation is a set of approaches meant for deciding which segmentation is good. In this study, we tested different supervised segmentation evaluation measures and visual interpretation in the case of boreal forest habitat mapping in Southern Finland. The data used were WorldView-2 satellite imagery, a lidar digital elevation model (DEM), and a canopy height model (CHM) in 2 m resolution. The segmentation methods tested were the fractal net evolution approach (FNEA) and IDRISI watershed segmentation. Overall, 252 different segmentation methods, layers, and parameter combinations were tested. We also used eight different habitat delineations as reference polygons agains…

luokitus (toiminta)Watershedbusiness.industryComputer scienceSegmentation-based object categorizationta1172ta1171Scale-space segmentationImage segmentationMachine learningcomputer.software_genreRandom forestsegmentointiRankingGeneral Earth and Planetary SciencesSegmentationArtificial intelligencekaukokartoitusbusinessDigital elevation modelcomputerlidarlaserkeilausluokitusInternational Journal of Remote Sensing
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Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

2019

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.

medicine.medical_specialtyGeospatial analysisComputer sciencehyperspectral imagingSciencecomputer.software_genrehyperspectral imaging; point cloud; sensor integration; data fusion; machine learning; deep learning; classification; estimation; semantic segmentation; object detection; point cloud filteringmedicine3D-mallinnussensor integrationpoint cloud filteringdata fusionestimationbusiness.industryDeep learningspektrikuvausQHyperspectral imagingdeep learningobject detectionSensor fusionObject (computer science)Data scienceObject detectionsemantic segmentationSpectral imagingVariety (cybernetics)classificationpoint cloud filteringsegmentointikoneoppiminenmachine learningclassificationGeneral Earth and Planetary SciencesArtificial intelligencekaukokartoitusbusinesscomputerpoint cloudRemote Sensing
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Potential of using data assimilation to support forest planning

2017

Uncertainty in forest information typically results in economic and ecological losses as a consequence of suboptimal management decisions. Several techniques have been proposed to handle such uncertainties. However, these techniques are often complex and costly. Data assimilation (DA) has recently been advocated as a tool that may reduce the uncertainty, thereby improving the quality of forest planning results. It offers an opportunity to make use of all new sources of information in a systematic way and thus provides more accurate and up-to-date information to forest planning. In this study, we refer to literature on handling uncertainties in forest planning, as well as related literature …

optimointibayesilainen menetelmäsuboptimal lossmetsäsuunnittelukaukokartoitusepävarmuus
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HYPERBLEND: SIMULATING SPECTRAL REFLECTANCE AND TRANSMITTANCE OF LEAF TISSUE WITH BLENDER

2022

Abstract. Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral imag…

remote sensingopen sourceavoin lähdekoodiray tracinghyperspectral imagingreflektanssisimulointikasvillisuuskaukokartoitusleaf optical properties model3D-mallinnussimulationhyperspektrikuvantaminen
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Developing and comparing methods for mapping habitat types and conservation values using remote sensing data and GIS methods

2014

segmentationairborne laser scanningconservation valuehabitaattiobjektiperustainen kuva-analyysielinympäristötyypitsegmentointilajirunsauselinympäristöluokitteluobject-based image analysishabitat typespectral imageskaukokartoitusspecies richnessmetsämaisemapaikkatietomenetelmätsuojeluarvotluokittelumenetelmätlaserkeilaus
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Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spe…

2016

Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital sur…

spectroscopygeometry010504 meteorology & atmospheric sciencesInfraredspektroskopiata11710211 other engineering and technologiesGeometryradiometry02 engineering and technologyremotely piloted aircraft01 natural scienceskalibrointiremote sensingCalibrationgeographic information systemComputer visionElectrical and Electronic Engineeringta218021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingta113ta213Contextual image classificationbusiness.industryHyperspectral imagingOtaNanocalibrationstereo visionVNIRInterferometryGeneral Earth and Planetary SciencesRGB color modelEnvironmental scienceRadiometrygeometriakaukokartoitusArtificial intelligencebusinessimage classificationIEEE Transactions on Geoscience and Remote Sensing
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