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Remote sensing data and satellite images are broadly used for land cover information. There are so many challenges to classify pixels on the basis of features and characteristics. Generally it is pixe...
Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels....
Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods ...
Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classificatio...
In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume ...
This paper proposed a method of unsupervised classification of polarimetric SAR data based on image clustering and H/A/a decomposition. Fully polarimetric L band data collected by ALOS PALSAR system w...
The unsupervised change detection of multitemporal SAR usually can get different results through different threshold selection algorithms. It is hard to determine what kinds of results are the best. I...
Using recently developed techniques, a significant amount of Information could be extracted from spectral signatures of surface material in their continuous spectra. One of the main applications in wh...
In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of differen...
Traditionally, an unsupervised classification divides all pixels within an image into a corresponding class pixel by pixel; the number of clusters usually needs to be fixed a priori by a human analyst...
Traditionally, an unsupervised classification divides all pixels within an image into a corresponding class pixel by pixel; the number of clusters usually needs to be fixed a priori by a human analyst...
In a recent studie (Greiwe and Ehlers, 2005), the author developed a decision based data fusion approach for the analysis of spatial and spectral high resolution image data of an urban scene. Image s...
Statistical analysis is a widely used traditional technique for classification and discrimination in remotely sensed images, which supposes that there is only one endmember in an image pixel. Howeve...
The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about environmental features; nevertheless, VHR images frequently contain sh...
Current research suggests that metrics of landscape pattern can act as indicators of ecological processes and biodiversity maintenance. Land cover maps, created through remote sensing, enable the eva...

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