| dc.contributor | GONZALO JORGE URCID SERRANO | |
| dc.creator | JUAN CARLOS VALDIVIEZO NAVARRO | |
| dc.date | 2007-09 | |
| dc.date.accessioned | 2023-07-25T16:22:03Z | |
| dc.date.available | 2023-07-25T16:22:03Z | |
| dc.identifier | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/668 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7805886 | |
| dc.description | The advances in image spectroscopy have been applied for Earth observation at
different wavelengths of the electromagnetic spectrum using aircrafts or satellite
systems. This new technology, known as hyperspectral remote sensing, has found
many applications in agriculture, mineral exploration and environmental monitoring
since images acquired by these devices register the constituent materials in
hundred of spectral bands. Each pixel in the image contains the spectral information
of the zone. However, processing these images can be a difficult task because
the spatial resolution of each pixel is in the order of meters, an area of such size that
can be composed of different materials. The following research presents an alternative
methodology to detect pixels in the image that best represent the spectrum
of one material with as little contamination of any other as possible. The detection
of these pixels, also called endmembers, represents the first step for image segmentation
and is based on morphological autoassociative memories and the property
of strong lattice independence between patterns. Morphological associative memories
and strong lattice independence are concepts based on lattice algebra. Our
procedure subdivides a hyperspectral image into regions looking for sets of strong
lattice independent pixels. These patterns will be identified as endmembers and
will be used for the construction of abundance maps. | |
| dc.format | application/pdf | |
| dc.language | spa | |
| dc.publisher | Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| dc.relation | citation:Valdiviezo-Navarro JC | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.subject | info:eu-repo/classification/Teledetección/Remote sensing | |
| dc.subject | info:eu-repo/classification/Espectroscopia de imagen/Imaging spectroscopy | |
| dc.subject | info:eu-repo/classification/cti/1 | |
| dc.subject | info:eu-repo/classification/cti/22 | |
| dc.subject | info:eu-repo/classification/cti/2209 | |
| dc.subject | info:eu-repo/classification/cti/220990 | |
| dc.subject | info:eu-repo/classification/cti/220990 | |
| dc.title | Segmentación de imágenes hiperespectrales usando memorias asociativas morfológicas | |
| dc.type | info:eu-repo/semantics/masterThesis | |
| dc.type | info:eu-repo/semantics/acceptedVersion | |
| dc.audience | students | |
| dc.audience | researchers | |
| dc.audience | generalPublic | |
