Detection of Vegetation Spectral Signatures in Hyperspectral Images using Artificial Neural Networks
DOI:
https://doi.org/10.15837/ijccc.2026.2.7229Keywords:
Vegetation detection, Hyperspectral imaging, Environmental monitoring, Artificial neural networks, Remote sensingAbstract
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.
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