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The Fuzzy ARTMAP is a supervised learning method, providing high accuracy in many classifications. In this paper, we explore the role of hardware accelerators in remote sensing classification missions. We focus on the designing and implementing a massively parallel hardware architecture on a field-programmable gate array (FPGA) of the performance phase's algorithm. The implementation is mapped on...
The aim of this paper is to look for efficient algorithms allowing to deal with taking into account the modelization and the cartography of the sea components. Through the analysis carried on the family of Growing Neural Gas with an evolutive (scalable) architecture and with non supervised competitive learning, some modifications and improvements (modified IGNG) have been brought in order to associate...
In this paper we propose a hardware architecture of the Fuzzy ART's generalization applied to the classification of multi-spectral image acquired by the Algerian satellite ALSAT 1. The developed solution is original and exploits the maximum benefit of the embedded resources in the reconfigurable circuits FPGAs. The goal is the research of a scalable architecture which gives a very good quality of...
Remote sensing satellites produce images in two modes: panchromatic with very high spatial resolution, and multi-spectral mode with a spatial resolution lower. The purpose of this paper is to develop a method which combines the two modes. We built a new image with the spectral properties of the low-resolution image and the spatial detail of the high resolution image. The images used are derived from...
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