Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Digital elevation model (DEM) is primarily a way of visualising 2D maps, photographs and images in 3D. Common uses of DEMs are creation of relief maps, rendering of 3D visualizations, rectification of satellites images and aerial photographs, creation of different physical models, etc. DEMs can be produced by different methods. In this study, DEMs are produced by 1:25000 digital topographic maps,...
Classification is a well known of the significant tools used to recognize and examine most sharp information in images. Satellite image processing has become popular in these days because of benefits that those are giving. In any remote sensing particularly, the decision-making way mainly rely on the efficiency of the classification process. Image classification was performed generally and the classification...
Complex terrains (mountains) of varying elevations cover approximately 25% of the total global land area wherein nearly 26% of the world's population resides. Complex terrains offer difficulty in accurate owing to their inaccessibility and location constraint to carry out ground based survey. Digital Elevation Model (DEM) offers a reliable alternative to study the orography from remote sensing measurements...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
The use of multisource data in remote sensing image classification has become increasingly popular. Although additional features incorporated could improve classification accuracy, the amount of relevant information may induce interclass confusion. Feature selection plays an important role in image analysis process. This study investigates feature space optimization in the use of multispectral UltraCAM...
Urban expansion monitoring and organization can be performed through space-based observation thanks to the revisit time and level of details guaranteed by satellite remote sensing. In particular, the Landsat mission products are the most used thanks to the long time coverage and open access policy. This paper proposed a hybrid method — combination of pixel- and object-based analysis — in order to...
Change detection is by definition the capability to detect and highlight changes occurring in space and time. Earth Observation satellites represent a fundamental source of information thanks to repeatability in time and spatial resolution. In this paper, we propose an unsupervised change detection technique capable of processing a series of single-date built-up area extractions with two main goals:...
Impervious surface is considered as a key indicator of urban environment quality and urbanization. Many studies have attempted to extract urban impervious surface using multispectral images of medium and high resolution, especially images acquired in summer, as they achieved higher accuracy. However, the use of summer images may underestimate impervious surface area, since some impervious surfaces...
Remote sensing data have been commonly used for agricultural crop monitoring. This paper was assessed the quality of using SAR and optical data fusion for maize classification. Two different SAR data sets from different sensors including dual polarization (HH and VV) X-band COSMO-SkyMed (CSK) and quad polarization (HH, HV, VH and VV) C-band RADARSAT-2 images were fused with THAICHOTE (namely, THEOS,...
Positive and unlabeled learning (PUL) algorithm, an one-class classifier which is trained by positive samples and unlabeled samples, has been used in remote sensing classification. However, the effect of training strategy of PUL has not been investigated. This study tested the performances of PUL-SVM on cropland mapping by Landsat TM data using the training samples with different sizes and different...
Vegetation plays an important role in the terrestrial ecosystems. Monitoring vegetation is of great importance in understanding the climate change. Passive microwave remote sensing behaves as an attractive technique due to its penetrability and comprehensive macro scale. It is of great significance to develop a good forward model with simple form and high accuracy for the inversion. The commonly used...
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical...
The performance of pattern classifiers depends on the separability of the classes in the feature space — a property related to the quality of the descriptors — and the choice of informative training samples for user labeling — a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We...
This paper presents a novel classification method for high-spatial-resolution satellite scene classification introducing multiset aggregated canonical correlation analysis (MACCA)-based feature fusion to fuse and combine multiple features. Firstly, a superpixel representation of the scene is constructed by employing a high-efficiency linear iterative clustering algorithm. After that, three diverse...
Big Data Analytics methods take advantage of techniques from the fields of data mining, machine learning, or statistics with a focus on analysing large quantities of data (aka ‘big datasets’) with modern technologies. Big data sets appear in remote sensing in the sense of large volumes, but also in the sense of an ever increasing amount of spectral bands (i.e., high-dimensional data). The remote sensing...
Post-Classification Comparison(PCC) method is widely used in change detection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during change detection, which leads to the excessive evaluation of changed types and quantity. To solve this problem, this paper proposes a change detection method for remote sensing images...
In this paper, we propose a new approach to pixel and parcel-based classification of multi-temporal optical satellite imagery. We first restore missing data due to clouds and shadows based on vector and raster data fusion in different phases of classification methodology. Pixel-based classification maps are derived from an ensemble of neural networks, in particular multilayer perceptrons (MLPs). The...
The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for urban land cover in Wuhan, Hubei province of China. Firstly, radiation rectification, normalization processing and geometry registration are made between the bi-temporal images. Secondly, SVM approach is used in...
In this paper, an ontology-based framework of China Geographic National Conditions Monitoring (CGNCM) land cover extraction is presented. Using statistical analysis method, spectral, texture, spatial features of each land cover class are extracted from the referenced ZY-3 image acquired in summer, 2012. Stored in the form of OWL, the features of one class can be considered as the prototype of this...
Based on the measured spectra in the research area of Guangting reservoir, we build the model to retrieve chlorophyll-a, suspended solid and yellow substance. The paper mainly achieved the following results: we adopted matrix inversion method and L-M & NN method to analyse the water quality parameters, the selection schemes of the spectral band include REF, DER, RAN method, and then use the Guanting...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.