Image sequence segmentation based on 2D temporal entropic thresholding
Pattern Recognition Letters
Spatiotemporal Segmentation Based on Region Merging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion detection using Fourier image reconstruction
Pattern Recognition Letters
Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
Motion detection via change-point detection for cumulative histograms of ratio images
Pattern Recognition Letters
Performance measures for object detection evaluation
Pattern Recognition Letters
Multi-resolution region-based clustering for urban analysis
International Journal of Remote Sensing - Spatial Information Retrieval, Analysis, Reasoning and Modelling
Hierarchical segmentation of multiresolution remote sensing images
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
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Satellite image time series (SITS) analysis is an important domain with various applications in land study. In the coming years, both high temporal and high spatial resolution SITS will become available. In the classical methodologies, SITS are studied by analyzing the radiometric evolution of the pixels with time. When dealing with high spatial resolution images, object-based approaches are generally used in order to exploit the spatial relationships of the data. However, these approaches require a segmentation step to provide contextual information about the pixels. Even if the segmentation of single images is widely studied, its generalization to series of images remains an open-issue. This article aims at providing both temporal and spatial analysis of SITS. We propose first segmenting each image of the series, and then using these segmentations in order to characterize each pixel of the data with a spatial dimension (i.e., with contextual information). Providing spatially characterized pixels, pixel-based temporal analysis can be performed. Experiments carried out with this methodology show the relevance of this approach and the significance of the resulting extracted patterns in the context of the analysis of SITS.