Lajiao Chen

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A large group of dictionary learning algorithms focus on adaptive sparse representation of data. Almost all of them fix the number of atoms in iterations and use unfeasible schemes to update atoms in the dictionary learning process. It's difficult, therefore, for them to train a dictionary from Big Data. A new dictionary learning algorithm is proposed here(More)
Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation(More)
With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from(More)
Remote sensing image processing is characterized with features of massive data processing, intensive computation, and complex processing algorithms. These characteristics make the rapid processing of remote sensing images very difficult and inefficient. The rapid development of general-purpose graphic process unit (GPGPU) computing technology has resulted(More)
s u m m a r y This paper presents a modeling approach to simulate runoff and soil erosion at the small watersheds of the Three-Gorge Reservoir drainage area in China by using limited plot data on runoff-soil erosion. The approach coupled the empirical relationships between soil loss and runoff. This relationship is derived from the experimental plots under(More)