STARS: A New Method for Multitemporal Remote Sensing

There is great potential for the development of remote sensing methods that integrate and exploit both multispectral and multitemporal information. This paper presents a new image processing method: Spectral–Temporal Analysis by Response Surface (STARS), which synthesizes the full information content of a multitemporal–multispectral remote sensing image data set to represent the spectral variation over time of features on the Earth's surface. Depending on the application, STARS can be effectively implemented using a range of different models [e.g., polynomial trend surface (PTS) and collocation surface (CS)], exploiting data from different sensors, with varying spectral wavebands and acquiring data at irregular time intervals. A case study was used to test STARS, evaluating its potential to characterize sugarcane harvest practices in Brazil, specifically with and without preharvest straw burning. Although the CS model presented sharper and more defined spectral–temporal surfaces, abrupt changes related to the sugarcane harvest event were also well characterized with the PTS model when a suitable degree was set. Orthonormal coefficients were tested for both the PTS and CS models and performed more accurately than regular coefficients when used as input for three evaluated classifiers: instance based, decision tree, and neural network. Results show that STARS holds considerable potential for representing the spectral changes over time of features on the Earth's surface, thus becoming an effective image processing method, which is useful not only for classification purposes but also for other applications such as understanding land-cover change. The STARS algorithm can be found at www.dsr.inpe.br/~mello.