Читать книгу Change Detection and Image Time Series Analysis 2 - Группа авторов - Страница 9

Accurate and time-efficient classification methods for multitemporal imagery and satellite image time series are important tools required to support the rapid and reliable extraction of information on a monitored region, especially when an extensive area is considered. Given the substantial amount and variety of data currently available from last-generation, very-high spatial resolution satellite missions, the main difficulty is developing a classifier that uses the benefits of input time series that are possibly composed of multimission, multisensor, multiresolution and multifrequency imagery (Gómez-Chova et al. 2015). From an application-oriented viewpoint, the goal is to take advantage of this variety of input sources, in order to maximize the accuracy and effectiveness of the resulting thematic mapping products. From a methodological viewpoint, this goal aims for the development of novel data fusion techniques. These techniques should be flexible enough to support the joint classification of a time series of images collected in the same area, by different sensors, at different times, and associated with multiple spatial resolutions and wavelength ranges.

In this chapter, this joint fusion problem is addressed. First, an overview of the major concepts and of the recent literature in the area of remote sensing data fusion is presented (see section 1.1.3). Then, two advanced methods for the joint supervised classification of multimission image time series, including multisensor optical and Synthetic Aperture Radar (SAR) components acquired at multiple spatial resolutions, are described (see section 1.2). The two techniques address different problems of supervised classification of satellite image time series and share a common methodological formulation based on hierarchical Markov random field (MRF) models. Examples of the experimental results obtained by the proposed approaches in the application to very-high-resolution time series are also presented and discussed (see section 1.3).

On the one hand, the use of multiresolution and multiband imagery has been previously shown to optimize the classification results in terms of accuracy and computation time. On the other hand, the integration of the temporal dimension into a classification scheme can both enhance the results in terms of reliability and capture the evolution in time of the monitored area. However, the joint problem of the fusion of several distinct data modalities (e.g. multitemporal, multiresolution and multisensor) has been much more scarcely addressed in the remote sensing literature so far.