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A STUDY ON THE SAR DATA OBSERVATION TIME FOR THE CLASSIFICATION OF PLANTING CONDITION OF PADDY FIELDS
Paddy TerraSAR-X Classification Observation time Backscattering Polarization
2016/12/1
In recent years, cultivation methods of rice have been diversified due to the low cost of rice-growing techniques. For example, there is direct sowing of seed rice in paddy field in addition to the pr...
LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION
Airborne laser scanning support vector machine landslide mapping Pricncipal Component Analysis
2016/11/30
Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and secon...
THE USAGE OF RUSBOOST BOOSTING METHOD FOR CLASSIFICATION OF IMPERVIOUS SURFACES
RUSboost Classification tree Impervious surface Image classification Remote sensing
2016/11/24
Impervious surface areas are artificial structures covered by materials such as asphalt, stone, brick, rooftops and concrete. Buildings, parking lots, roads, driveways and sidewalks are shown as imper...
CLASSIFICATION OF CROPLANDS THROUGH FUSION OF OPTICAL AND SAR TIME SERIES DATA
Multi-temporal Data fusion Landsat 5 Radarsat-1 Machine learning Crop classification Paddy rice index
2016/11/24
Many satellite sensors including Landsat series have been extensively used for land cover classification. Studies have been conducted to mitigate classification problems associated with the use of sin...
SEGMENTATION AND CLASSIFICATION OF NEPAL EARTHQUAKE INDUCED LANDSLIDES USING SENTINEL-1 PRODUCT
Sentinel-1 Landslide Earthquake SAR Polarimetry Texture Segmentation Classification Nepal
2016/11/24
On April 26, 2015, an earthquake of magnitude 7.8 on the Richter scale occurred, with epicentre at Barpak (28°12'20''N,84°44'19''E), Nepal. Landslides induced due to the earthquake and its aftershock ...
COMPARISON BETWEEN SPECTRAL,SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL–1 IMAGES
Sentinel – 1 Urban Polarimetry Texture Classification Landcover
2016/11/24
Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper ...
COMPARISON OF C-BAND AND X-BAND POLARIMETRIC SAR DATA FOR RIVER ICE CLASSIFICATION ON THE PEACE RIVER
River ice dynamics SAR Polarimetry
2016/11/23
In this study, synthetic aperture radar (SAR) data from TerraSAR-X were compared with RADARSAT-2 data to evaluate their effectiveness for river ice monitoring on the Peace River. For several years RAD...
LAND USE CLASSIFICATION FROM VHR AERIAL IMAGES USING INVARIANT COLOUR COMPONENTS AND TEXTURE
High resolution colour images texture classification invariant colour spaces aerial images
2016/11/23
Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number ...
PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY
Hyperspectral Extended Morphological Profile (EMP) spectral-spatial classification parallel processing GPU
2016/11/23
Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. H...
AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION
remote sensing classification texture analysis granulometry VHR satellite image vegetation indices
2016/11/23
In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of ...
BENCHMARK OF MACHINE LEARNING METHODS FOR CLASSIFICATION OF A SENTINEL-2 IMAGE
Machine learning Sentinel-2 Remote sensing Neural nets Agriculture Land cover Classification
2016/11/23
Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categor...
POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
Improved Freeman decomposition Gaussian Mixture Model Polarimetric SAR data Iteration desorientation EM algorithm Unsupervised classification
2016/11/23
Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most ...
PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH OBJECT-BASED IMAGE CLASSIFICATION
Ercis-Van Earthquake Object-Based Image Classification Building Damage Detection
2016/11/23
Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very import...
TOPIC MODELLING FOR OBJECT-BASED CLASSIFICATION OF VHR SATELLITE IMAGES BASED ON MULTISCALE SEGMENTATIONS
Topic modelling Image classification Object-based Multiscale segmentation
2016/11/23
Multiscale segmentation is a key prerequisite step for object-based classification methods. However, it is often not possible to determine a sole optimal scale for the image to be classified because i...
A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION
VHSR remote sensing image Classification Support vector machine (SVM) Composite kernel Latent Dirichlet allocation (LDA) Structure Spatial Spectral
2016/11/23
A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure inf...