Constrained optical flow for aerial image change detection

Abstract

Nowadays, the amount of video data acquired for observation or surveillance applications is overwhelming. Due to these huge volumes of video data, focusing the attention of operators on "areas of interest" requires change detection algorithms. In the particular task of aerial observation, camera motion and viewpoint differences introduce parallax effects, which may substantially affect the reliability and the efficiency of automatic change detection.In this paper, we introduce a novel approach for change detection that considers the geometric aspects of camera sensors as well as the statistical properties of changes. Indeed, our method is based on optical flow matching, constrained by the epipolar geometry, and combined with a statistical change decision criterion. The good performance of our method is demonstrated through our new public Aerial Imagery Change Detection (AICD) dataset of labeled aerial images.

Change Detection via Selective Guided Contrasting Filters

“…Results of quantitative experiments demonstrates that comparative filtering based on guided contrasting with considered variations of thresholded LSC and SO parameters is generally better, than approaches (Vizilter et al, 2015), (Bourdis et al,2011), excepting filter with MSMCC as LSC. As noted above (sect.4.1) we observe low robustness because there is the segmentation procedure for forming the mosaic shapes (4) of input images in the pipeline.…”

“…In our experiments with proposed change detection pipeline for long-range remote sensing we use the public Change Detection dataset introduced in (Bourdis et al, 2011) (Fig.5). This dataset contains 1000 pairs of 800x600 simulated aerial images and 1000 corresponding 800x600 ground truth masks.…”

“…We select a subset of 100 reference and test image pairs for 50 different scenes with 0 degrees relative camera angle. As proposed in (Bourdis et al, 2011) we compare the detection results with respect to the ground truth at pixel level, but calculate the precision and recall values at the object (region) level. In order to do this, we from the list of ground truth objects and list of detected objects (accepted regions of filtered binarized morphological difference map).…”

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

“…Synthetic datasets and visual modelling tools. To the best of our knowledge, AICD dataset [12] is the only published synthetic dataset on change detection in remote sensing domain. It consists of 1000 pairs of 800 × 600 images.…”

Demolished building detection from aerial imagery using deep learning

“…However, accurate change detection of buildings still remains a challenging task because of the different characteristics of the two images that arise due to differences in camera, atmosphere, or shadows. Previous studies on building change detection have mainly focused on the spectral information and color variations in bi-temporal images (Bourdis et al, 2011). However, these methods yield too many errors, including mis-detection (i.e., a demolished building going undetected), over-detection (i.e., an undemolished building being detected as demolished, or a lot of changes in non-building area.…”