Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

a Center for Remote Sensing, Department of Geography and Environment, Boston University, Boston, MA, USA b State Key Laboratory of Remote Sensing Science, Center for Remote Sensing and GIS of Geography College, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing, China c Department of Earth and Environmental Studies, Montclair State Un...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

With the development of the remote sensing technology, high resolution remote sensing images widely penetrates into the common people’s life. Traditional medium or low resolution image processing method based on pixel doesn't meet people’s requirement any more. In view of it, this paper puts forward a high resolution remote sensing image segmentation method based on the traditional watershed al...

This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years s...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

the complexity of urban systems in our recent world, make it difficult to evaluation and explanation of their changes with traditional and customarily methods. in the past, remote sensing has shown an ability to detect and describe urban growth patterns at different spatial scales. with new remote sensing devices (e.g. ikonos), with innovative image processing techniques and with further develo...