Data fusion approach for change detection in multi-temporal ERS-SAR images

Abstract

This paper presents a new method to detect changes in multi-temporal satellite SAR images. The proposed approach consists in an extraction step -specific change measures reveal the presence of changing features -followed by a fusion step where these measures and a priori information such as geographical map are merged to detect changes. The method is applied to the detection of deforested areas in tropical rain forest of French Guiana. Results obtained with SAR images from satellites ERS1/2 are presented at two different scales: on a macroscopic scale for the detection of zones presenting a strong deforestation probability and on a microscopic scale for a segmentation of the main deforested parcels.

Forest/Nonforest Segmentation Using Sentinel-1 and -2 Data Fusion in the Bajo Cauca Subregion in Colombia

“…The former uses pixel-to-pixel analysis, so it needs speckle noise filtering to reduce false detections [16]. The latter uses segmentation strategies and reduces the effect of speckle noise and slight variations on the change detection [12,25].…”

“…Works such as the one presented in [26] are a good example of recent attempts to use combinations of Sentinel-1 bands and Sentinel-2 vegetation indices for similar applications like the determination of forest structure in broad scales. One of the identified potential areas of research is in the study and identification of suitable sensor combinations, e.g., multispectral/SAR [5,19,23,[27][28][29] or between sensors or SAR features only [6,11,25,[30][31][32].…”