Effects of Different Methods of Radiometric Calibration on the Use of Training Data for Supervised Classification of Landsat5/TM Images from other Dates

Reis, Mariane Souza; Pantaleão, Eliana; Dutra, Luciano Vieira; Sant’Anna, Sidnei J. S.; Escada, Maria Isabel Sobral

doi:10.1109/igarss.2019.8898150

Cited by 2 publications

(1 citation statement)

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“…The first one is the use of land cover training samples collected for each image to be classified which may be very expensive for studies with a high number of images. However, parallel studies show good results in using the spectral information from one Landsat5/TM image to train an ML classifier that will be used to classify a different calibrated image from the same sensor [29]. This process is known as signature extension, spectral extensibility or generalization of training samples [30].…”

Section: Discussionmentioning

confidence: 99%

Avoiding Invalid Transitions in Land Cover Trajectory Classification With a Compound Maximum a Posteriori Approach

et al. 2020

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Classifying remote sensing time-series in land cover trajectories can provide essential information about ecosystems functioning and about the impacts of natural phenomena or human activities over the environment. The existing approaches commonly used for this task can present serious drawbacks, such as the possibility to derive invalid trajectories, use of complex data inputs, and several classification steps. To provide a simple method for land cover trajectory classification, we present a novel algorithm named Compound Maximum a Posteriori (CMAP) classifier. CMAP incorporates the knowledge of land cover dynamics and the information of multi-temporal data sets to produce only valid land cover trajectories using a global generative classification approach and simple inputs. CMAP was tested in two case studies, in which we compared land cover trajectories obtained by CMAP to those obtained using the traditional Maximum Likelihood (ML) classifier in a post-classification comparison approach. In the first case study, we classified 6 images from the same sensor, using the same land cover legend. In the second case study, we classified 3 images from different types of sensors, using different land cover legend levels. Because of its formulation, CMAP does not return invalid transitions/trajectories as classification results. The use of ML and postclassification comparison, on the other hand, resulted in invalid land cover trajectories in more than 50% of the used images in both case studies. Furthermore, the use of CMAP leads to better accuracy indexes for land cover classification of each date and reduces the classification noise. INDEX TERMS Classification algorithms, land cover trajectory mapping, multi-temporal classification, remote sensing, remote monitoring.

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Section: Discussionmentioning

confidence: 99%