Leveraging autoencoders in change vector analysis of optical satellite images

• Published in: Journal of intelligent information systems 2022-06, Vol.58 (3), p.433-452
• Main Authors: Andresini, Giuseppina, Appice, Annalisa, Iaia, Daniele, Malerba, Donato, Taggio, Nicolò, Aiello, Antonello
• Format: Article
• Language: English
• Subjects: Alarms; Algorithms; Artificial Intelligence; Artificial neural networks; Change detection; Computer Science; Data Structures and Information Theory; False alarms; Hyperspectral imaging; Image acquisition; Image reconstruction; Image restoration; Information Storage and Retrieval; Information systems; IT in Business; Machine learning; Methods; National parks; Natural Language Processing (NLP); Neural networks; Remote sensing; Satellite imagery; Vector analysis
• ISSN: 0925-9902; 1573-7675
• DOI: 10.1007/s10844-021-00670-9

Various applications in remote sensing demand automatic detection of changes in optical satellite images of the same scene acquired over time. This paper investigates how to leverage autoencoders in change vector analysis, in order to better delineate possible changes in a couple of co-registered, optical satellite images. Let us consider both a primary image and a secondary image acquired over time in the same scene. First an autoencoder artificial neural network is trained on the primary image. Then the reconstruction of both images is restored via the trained autoencoder so that the spectral angle distance can be computed pixelwise on the reconstructed data vectors. Finally, a threshold algorithm is used to automatically separate the foreground changed pixels from the unchanged background. The assessment of the proposed method is performed in three couples of benchmark hyperspectral images using different criteria, such as overall accuracy, missed alarms and false alarms. In addition, the method supplies promising results in the analysis of a couple of multispectral images of the burned area in the Majella National Park (Italy).