Land-Cover Change Detection Using Local Feature Descriptors Extracted From Spectral Indices

Abstract

An effective monitoring and analysis of ecosystems requires developing new tools and knowledge. In this paper, we propose an approach for detecting land-cover changes using satellite Image Time Series. This approach represents each image by spectral indices and then extracts local features of these representations. Next, a clustering technique (e.g., k-means) is applied to the extracted features, where the resulting clusters are assumed to refer to land-cover classes. The land-cover change is then obtained by counting the number of times an assigned class to each point changes along the time series. For our experiments, we use a collection of Landsat-5 images captured every second month from October 2009 to August 2010 over the protected area of the Doñana National Park in southwestern Spain, which is the largest sanctuary for migratory birds in western Europe. Results demonstrate that the proposed approach can detect the occurring changes in the main land-cover categories along the assessed time series.