24,211 research outputs found
Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image
The research scope of this paper is to apply spatial object based image
analysis (OBIA) method for processing panchromatic multispectral image covering
study area of Brussels for urban mapping. The aim is to map different land
cover types and more specifically, built-up areas from the very high resolution
(VHR) satellite image using OBIA approach. A case study covers urban landscapes
in the eastern areas of the city of Brussels, Belgium. Technically, this
research was performed in eCognition raster processing software demonstrating
excellent results of image segmentation and classification. The tools embedded
in eCognition enabled to perform image segmentation and objects classification
processes in a semi-automated regime, which is useful for the city planning,
spatial analysis and urban growth analysis. The combination of the OBIA method
together with technical tools of the eCognition demonstrated applicability of
this method for urban mapping in densely populated areas, e.g. in megapolis and
capital cities. The methodology included multiresolution segmentation and
classification of the created objects.Comment: 6 pages, 12 figures, INSO2015, Ed. by A. Girgvliani et al. Akaki
Tsereteli State University, Kutaisi (Imereti), Georgi
Impervious surface estimation using remote sensing images and gis : how accurate is the estimate at subdivision level?
Impervious surface has long been accepted as a key environmental indicator linking development to its impacts on water. Many have suggested that there is a direct correlation between degree of imperviousness and both quantity and quality of water. Quantifying the amount of impervious surface, however, remains difficult and tedious especially in urban areas. Lately more efforts have been focused on the application of remote sensing and GIS technologies in assessing the amount of impervious surface and many have reported promising results at various pixel levels. This paper discusses an attempt at estimating the amount of impervious surface at subdivision level using remote sensing images and GIS techniques. Using Landsat ETM+ images and GIS techniques, a regression tree model is first developed for estimating pixel imperviousness. GIS zonal functions are then used to estimate the amount of impervious surface for a sample of subdivisions. The accuracy of the model is evaluated by comparing the model-predicted imperviousness to digitized imperviousness at the subdivision level. The paper then concludes with a discussion on the convenience and accuracy of using the method to estimate imperviousness for large areas
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has led to the
development of new techniques for digital pattern classification. Very recently, deep learning (DL)
models have emerged as a powerful solution to approach many machine learning (ML) problems.
In particular, convolutional neural networks (CNNs) are currently the state of the art for many image
classification tasks. While there exist several promising proposals on the application of CNNs to
LULC classification, the validation framework proposed for the comparison of different methods
could be improved with the use of a standard validation procedure for ML based on cross-validation
and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed
architecture and parametrization, to achieve high accuracy on LULC classification over RS data
from different sources such as radar and hyperspectral. We also present a methodology to perform
a rigorous experimental comparison between our proposed DL method and other ML algorithms
such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out
demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance
for all the datasets studied, regardless of their different characteristics.Ministerio de EconomĂa y Competitividad TIN2014-55894-C2-1-RMinisterio de EconomĂa y Competitividad TIN2017-88209-C2-2-
Benchmark of machine learning methods for classification of a Sentinel-2 image
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 categories, i.e. image classification. This is a very challenging issue
since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and
orientations.
In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and
classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear
discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered
perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an
independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution
images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few
samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree
plantations (v) grasslands.
Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the
training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five
accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of
data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from
validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from
0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its
ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable
performanc
Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach
While small, fragmented wooded elements do not represent a large surface area in
agricultural landscape, their role in the sustainability of ecological processes is
recognized widely. Unfortunately, landscape ecology studies suffer from the lack
of methods for automatic detection of these elements. We propose a hybrid
approach using both aerial photographs and ancillary data of coarser resolution
to automatically discriminate small wooded elements. First, a spectral and textural
analysis is performed to identify all the planted-tree areas in the digital photograph.
Secondly, an object-orientated spatial analysis using the two data sources
and including a multi-resolution segmentation is applied to distinguish between
large and small woods, copses, hedgerows and scattered trees. The results show the
usefulness of the hybrid approach and the prospects for future ecological
applications
Monitoring urban growth and land use land cover change in Al Ain, UAE using remote sensing and GIS techniques
Urbanization and industrialization cause a serious land degradation problem, including an increased pressure on natural resources such as deforestation, rise in temperature and management of water resources. The Urban Heat Island (UHI) effects of urbanization are widely acknowledged. Increase of impervious surface is a surrogate measure of urbanization and their effects on local hydrology is well reported in literature. This study investigates the spatial-temporal dynamics of land use and land cover changes in Al Ain, UAE, from 2006 to 2016. The Landsat images of two different periods, i.e., Landsat ETM of 2006 and Landsat 8 for 2016 were acquired from earth explorer site. Semi-supervised known as the hybrid classification method was used for image classification. The change detection was carried out through post-classification techniques. The study area was categorized into five major classes. These are agriculture, gardens, urban, sandy areas and mixed urban/sandy areas. It was observed that agricultural and urban land increases from 42,560 ha to 45,950 ha (8%) and 8150 ha to 9105 ha (12%), respectively. Consequently, the natural sandy area was reduced. It was also found that the urban area was expanded dramatically in the west and southwest directions. The outcomes of this study would help concerning authorities for a sustainable land and water resources management in the Al Ain region
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