Abstract

The scope of this dissertation is to present and discuss novel paradigms and techniques for the extraction of information from long time series of remotely sensed images. Many images are acquired everyday at high spatial and temporal resolution. The unprecedented availability of images is increasing due to the number of acquiring sensors. Nowadays, many satellites have been launched in orbit around our planet and more launches are planned in the future. Notable examples of currently operating remote sensing missions are the Landsat and Sentinel programs run by space agencies. This trend is speeding up every year with the launch of many other commercial satellites. Initiatives like cubesats propose a new paradigm to continuously monitor Earth’s surface. The larger availability of remotely sensed data does not only involve space-borne platforms. In the recent years, new platforms, such as airborne unmanned vehicles, gained popularity also thanks to the reduction of costs of these instruments. Overall, all these phenomena are fueling the so-called Big Data revolution in remote sensing. The unprecedented number of images enables a large number of applications related to the monitoring of the environment on a global and regional scale. A non-exhaustive list of applications contains climate change assessment, disaster monitoring and urban planning. In this thesis, novel paradigms and techniques are proposed for the automatic exploitation of the information acquired by the growing number of remote sensing data sources, either multispectral or Synthetic Aperture Radar (SAR) sensors. There is a need of new processing strategies being able to reliably and automatically extract information from the ever growing amount of images. In this context, this thesis focuses on Change Detection (CD) techniques capable of identifying areas within remote sensing images where the land-cover/land-use changed. Indeed, CD is one of the first steps needed to understand Earth’s surface dynamics and its evolution. Images from such long and dense time series have redundant information. So, the information extracted from one image or a single image pair in the time series is correlated to other images or image pairs. This thesis explores mechanisms to exploit the temporal correlation within long image time series for an improved information extraction. This concept is general and can be applied to any information extraction process. The thesis provides three main novel contributions to the state of the art. The first contribution consists in a novel framework for CD in image time series. The binary change variable is modeled as a conservative field. Then, it is used to improve the bi-temporal CD map computed between a target pair of images extracted from a time series. This framework takes advantage of the correlation of changes detected between pairs of images extracted from long time series. The second contribution presents an iterative approach that aims at improving the global CD performance for any possible pair of images defined within a time series. The results obtained by any bi-temporal technique, either binary or multiclass, are automatically validated against each other. By means of an iterative mechanism, the consistency of changes is tested and enforced for any pair of images. The third contribution consists in the detection of clouds in long time series of multispectral images and in the restoration of pixels covered by clouds. The presence of clouds may strongly affect the automatic analysis of images and the performance of change detection techniques (or other processes for the extraction of information). In this contribution, the temporal information of long optical image time series is exploited to improve the identification of pixels covered by clouds and their restoration with respect to standard monotemporal approaches. The effectiveness of the proposed approaches is proved on experiments on synthetic and real multispectral and SAR images. Experimental results are accompanied by comprehensive qualitative and quantitative analysis.