Land-cover evolution class analysis in Image Time Series of Landsat and Sentinel-2 based on Latent Dirichlet Allocation

Satellite Image Time Series (SITS) are widely used in monitoring the Earth's changes for various applications such as land-cover evolution analysis. In this paper, we propose an approach based on Latent Dirichlet Allocation (LDA) which considers spatial and spectral information to measure the land-cover changes in multispectral SITS. For our experiments, we focus on the vegetation dynamics of the Doñana National Park (in southwestern Spain) using a Landsat and a Sentinel-2 SITS dataset. The proposed approach represents each image by Normalized Difference Vegetation Index (NDVI) and tiles it into smaller patches. The patches are then modeled as Bag-of-Words (BoW) and LDA is applied to them in order to discover the latent structure of the image. The divergence between the latent structures of any two consecutive images is then considered as the measure of change. Results show that the changes measured by the proposed approach can represent the vegetation dynamics of the region of interest. Moreover, comparing the results obtained from the two datasets demonstrates that using high-level information allows the proposed approach to measure the changes independent of the sensor. This will support long-term monitoring through combining various available data.