{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:38:21Z","timestamp":1773355101405,"version":"3.50.1"},"reference-count":0,"publisher":"Agora University of Oradea","issue":"2","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INT J COMPUT COMMUN, Int. J. Comput. Commun. Control"],"abstract":"<jats:p>Considering the challenge in hyperspectral imaging of developing new computational methods that strike a balance between accurate material classification and computational complexity, this work proposes the design and tunability of a model based on a sequential artificial neural network (ANN) to classify vegetation in hyperspectral images with 380 bands. To carry out this research, an adaptation of the CRISP-DM methodology was used, structured into four phases: P1. Business and data understanding, P2. Data preparation, P3. Modeling and evaluation, and P4. Modl application. As a result, a sequential ANN model was developed, featuring 380 input layers and a single output layer, along with a set of dense layers containing 12, 8 and 4 artificial neurons. After 20 epochs, the model showed high performance and consistent behavior in the training and test sets under the experimental setup considered. The model was applied to a hyperspectral image of the Manga neighborhood in Cartagena, classifying 41.921% of the image pixels as vegetation. This percentage of points exceeds by 12.941% the percentage obtained by the spectral differential similarity method, in which less continuous point detections were observed. This method is a viable alternative for use in environmental monitoring systems, especially when applied in parallel to large-scale images.<\/jats:p>","DOI":"10.15837\/ijccc.2026.2.7229","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:53Z","timestamp":1773315953000},"source":"Crossref","is-referenced-by-count":0,"title":["Detection of Vegetation Spectral Signatures in Hyperspectral Images using Artificial Neural Networks"],"prefix":"10.15837","volume":"21","author":[{"given":"Manuel Alejandro","family":"Ospina Alarc\u00f3n","sequence":"first","affiliation":[]},{"given":"Gabriel El\u00edas","family":"Chanch\u00ed  Golondrino","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Saba","sequence":"additional","affiliation":[]}],"member":"6528","published-online":{"date-parts":[[2026,3,12]]},"container-title":["INTERNATIONAL JOURNAL OF COMPUTERS  COMMUNICATIONS &amp; CONTROL"],"original-title":[],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:54Z","timestamp":1773315954000},"score":1,"resource":{"primary":{"URL":"https:\/\/univagora.ro\/jour\/index.php\/ijccc\/article\/view\/7229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.15837\/ijccc.2026.2.7229","relation":{},"ISSN":["1841-9844","1841-9836"],"issn-type":[{"value":"1841-9844","type":"electronic"},{"value":"1841-9836","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}