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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,...
In the era of constantly increasing Earth Observation (EO) data collections, information extraction and data analysis should be enhanced with a multi-temporal component enabled by the temporal resolution of satellite missions and create handy, yet powerful tools for those applications involving monitoring of land cover. The image time series, as results of the satellite revisiting period, gives you...
Current satellite images and image time series provide us with detailed information about the state of our planet as well as about our technical infrastructure and human activities. These images allow us to learn more about local, regional, and global phenomena and events, including - if interpreted properly - their causes and effects. In particular, image time series provide specific information...
With a continuous increase in the number of Earth Observation satellites, leading to the development of satellite image time series (SITS), the number of algorithms for land cover analysis and monitoring has greatly expanded. This paper offers a new perspective in dynamic classification for SITS. Four similarity measures (correlation coefficient, Kullback–Leibler divergence, conditional information,...
In this paper, a patch based method for multi-temporal analysis of high resolution image is proposed. Conventionally, multi-temporal analysis performed at pixel level suffer from several restrictions, e.g., registration, bi-temporal analysis. To overcome these restrictions, two methods for multi-temporal analysis are proposed at patch level. One is for change detection in time series data by classifying...
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