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Satellite acquisitions from LANDSAT (LS) and CBERS programs are widely used in monitoring land cover dynamics. In the acquired products, clouds form opaque objects are obscuring parts of the scene and preventing a reliable extraction of information from these areas. Consequently, cloud shadows create similar problems, as the reflected intensity of the shadowed areas is highly reduced, generating additional...
In this paper we present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently the conditional probability for a land cover class. Conventionally the information is passed among direct spatial neighbors to improve classification accuracy. The inclusion...
Very High Resolution (VHR) multitemporal images show a residual misalignment even after applying effective state of the art co-registration. This residual misalignment is caused by the dissimilarities of the acquisition circumstances such as off-nadir angle of the sensor, stability of the acquisition platform, structure of the considered scene, and so on. This paper aims at mitigating the residual...
In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed...
In the field of precision agriculture (PA), Un-manned Aerial Vehicles (UAVs) are creating new opportunities for remotely assessing various characteristics of crops. In this paper, we present two main contributions that were evaluated on a novel application: mapping red clover ground cover (RCGC). First, we develop an integrated system for collecting, pre-processing and analyzing aerial data for the...
We investigate the performance evaluation of merging (fusing) the classification capabilities of classifiers for the land use analysis. For the fusion approach, we select the parametric and non-parametric classifiers. The set used includes: Bayesian Network, Multi-layer Perceptron (MLP), Support Vector Machines (SVM) and Random Forest. These classifiers are selected based on their good over-all performance...
Pakistan faces heavy revenue losses in terms of one of its major cash crop i.e. Tobacco, due to the unavailability of accurate statistics of the total tobacco production. During the cropping season, there are many competing crops along with tobacco in the neighboring fields — making tobacco identification a challenging task. This study considers a pilot region of interest that spans over 64844 hectares,...
Movement in the space is one of the basic preconditions for the tasks fulfilment of armies and rescue units. However one third of European territory is covered by forests. Forest stand is generally considered as a movement obstacle, however there are situation when it is necessary to carry out the movement across it. It is essential to know the information about the trunk density, their thickness,...
The P-band ultra-wideband synthetic aperture radar interferometry (InSAR) not only has the capability of foliage penetration, but also has the advantage in determining the absolute interferometric phase, which plays a key role in the InSAR data processing. In this paper, a novel absolute phase determination approach for P-band UWB InSAR is presented. A reference phase is generated from the registration...
Remote Sensing Image classification is one of the major research areas due to its wide spectrum of applications including natural terrain feature classification, land use monitoring, ground water exploration, environmental disaster assessment and urban planning etc. All these applications give a great success to terrain use but the only thing required is the proper classification of remotely sensed...
In this paper, a change detection method for remotely sensed satellite images based on a decision theoretic method is proposed. The proposed method works in two stages. In the first stage, a difference image was computed using change vector analysis (CVA) technique. For multispectral images, CVA technique works well as it uses all the bands of two multidate satellite images to compute the difference...
Remote Sensing is widely used for mapping of land cover and land use. Classification of image satellites is also done by using these mapping. In this paper the classifier proposed is the Probabilistic based Neural Network developed using MATLAB. The data for image classification is acquired over various parts of Mumbai region which is LISS-III. Probabilistic based neural network is a supervised classification...
This paper addresses the classification of multispectral remote-sensing images by the neural-network approach. In particular, an experimental comparison on the performances provided by different neural models for classifying multisensor remote-sensing data is reported. Four neural classifiers are considered in the comparison: the Multilayer Perceptron, Probabilistic Neural Networks, Radial Basis Function...
Remote sensing land-use scene classification has a wide range of applications including forestry, urban-growth analysis, and weather forecasting. This paper presents an effective image representation method, Gabor-filtering-based completed local binary patterns (GCLBP), for land-use scene classification. It employs the multi-orientation Gabor filters to capture the global texture information from...
Super-resolution mapping (SRM) aims to locate subpixel class fractions geographically in the area represented by a mixed pixel. The accuracy of small sub-pixel class patches are represented by the popular SRM method is explored. It is shown that the accuracy of predicted patch location from the Hopfield Neural of SRM is a function of patch size. Specifically, the accuracy with which patch location...
Different polarization incidences and/or inflections yield different polarization scattering powers. The problem of searching optimal polarization state for the maximal polarization receiving power (PRP) is of significantly meaningful. Usually, there are no analytic solutions to the problem and current numerical methods are time consuming. Aiming to reduce the computational time, fast optimization...
Remotely sensed data is only a key source of detection of Earth's surface changes or Land-Use/Land-Cover (LULC) monitoring. During past decades, a series of effective change detection techniques such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) and Post Classification Comparison (PCC), have been developed to observe the LULC vicissitudes. All aforesaid techniques performed very...
In the framework of the Urban Atlas 2012 production, this paper investigated a set of generative models (Maximum likelihood, k-means) and discriminative models (k Nearest Neighbors, Support Vector Machine and Neural Network) to extract urban-tree cover at a European scale. Based on SPOT-5 images and a training on a large coarse resolution dataset, this study tested the performance of these algorithms...
Recent developments in geotechnologies provide resources to propose innovative strategies for urban and environmental management, including remote sensing data and computational resources for processing them. With the main objective of identifying urban areas of illegal occupation, this work uses WorldView-2-sensor images and the InterIMAGE, an image interpretation software, based on knowledge, under...
Monitoring and analysis of the land and rapid environmental change, leads to the use of Land Use and Land Cover (LULC) classification approaches from remote sensing data. The main focus of this aper is to illustrate the practical approach to analysis and mapping of land use and land cover features using high resolution satellite images. The study is carried out for two different places, Basel and...
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