Parallel Spatiotemporal Autocorrelation and Visualization System for Large-Scale Remotely Sensed Images
Document Type
Article
Publication Date
1-1-2012
Abstract
Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering. This paper develops a Distributed Computing System for Remotely Sensed Images framework (DCSRI) to support distributed and high performance computing for geospatial images. A new algorithm supporting parallel computing with dynamic workload balance for large images, namely Variogram-based Image Texture Extractor (VITE) for extracting image texture from massive and dynamic digital remotely sensed images is presented. The VITE algorithm is used to represent and transform the original data into image textures. The DCSRI framework has the capacity to perform high performance computing on Linux clusters or supercomputers to address the intensive computing challenges arising from large and multiple images. Advanced web technology is also exploited to enable interactive user experience with prompt visual feedback as well as good interoperability. Users can also dynamically steer the visualization and computation process by adjusting the computing parameters on-the-fly. This system leads to a great reduction of image data and provides useful information for knowledge discovery and digital image classification in a user friendly and computing efficient way.
DOI
10.1007/s11227-010-0420-4
Montclair State University Digital Commons Citation
Zhu, Michelle; Wang, Guangxing; and Oyana, Tonny, "Parallel Spatiotemporal Autocorrelation and Visualization System for Large-Scale Remotely Sensed Images" (2012). Department of Computer Science Faculty Scholarship and Creative Works. 470.
https://digitalcommons.montclair.edu/compusci-facpubs/470