Abstract
Various applications in remote sensing demand automatic detection of changes in optical satellite images of the same scene acquired over time. This paper investigates how to leverage autoencoders in change vector analysis, in order to better delineate possible changes in a couple of co-registered, optical satellite images. Let us consider both a primary image and a secondary image acquired over time in the same scene. First an autoencoder artificial neural network is trained on the primary image. Then the reconstruction of both images is restored via the trained autoencoder so that the spectral angle distance can be computed pixelwise on the reconstructed data vectors. Finally, a threshold algorithm is used to automatically separate the foreground changed pixels from the unchanged background. The assessment of the proposed method is performed in three couples of benchmark hyperspectral images using different criteria, such as overall accuracy, missed alarms and false alarms. In addition, the method supplies promising results in the analysis of a couple of multispectral images of the burned area in the Majella National Park (Italy).
Highlights
The Earth’s surface is constantly changing due to anthropogenic and natural causes like the progression of desert areas, deforestation, glacier movements, fires or earthquakes (Alberti et al, 2003)
A threshold is always determined to separate the changed pixels from the unchanged background. Following this mainstream of research in change detection (CD), we propose a Change Vector Analysis (CVA) method, named ORCHESTRA, to analyse bi-temporal, co-registered MS/HS images of an Earth’s scene, which are denoted as primary image and secondary image, respectively
We explore how the autoencoder g ·f trained on the image of the couple assigned to the primary role can accurately reconstruct the primary image, while badly reconstructing the changed pixels of the secondary image
Summary
The Earth’s surface is constantly changing due to anthropogenic and natural causes like the progression of desert areas, deforestation, glacier movements, fires or earthquakes (Alberti et al, 2003). In the unsupervised machine learning paradigm (Bruzzone & Prieto, 2000; Hussain et al, 2013), changes are commonly detected by resorting to the Change Vector Analysis (CVA) strategy that bases on a reliable measure of distance (or similarity) computed between the two images In this strategy, a threshold is always determined to separate the changed pixels from the unchanged background. A threshold is always determined to separate the changed pixels from the unchanged background Following this mainstream of research in CD, we propose a CVA method, named ORCHESTRA (autOecodeR-based CHange dEtection in hyper SpecTRAl/ multispectral images), to analyse bi-temporal, co-registered MS/HS images of an Earth’s scene, which are denoted as primary image and secondary image, respectively.
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