Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification

Appice, Annalisa; Guccione, Pietro; Acciaro, Emilio; Malerba, Donato

doi:10.1007/s10489-020-01701-8

Cited by 11 publications

(24 citation statements)

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“…Then we compute pixelwise the distance between X and Y by resorting to the algorithm SAM that is commonly used in CVA methods (e.g. Appice et al, , 2020Lopez-Fandino et al, 2018). As pointed out in Seydi and Hasanlou (2017), the computation of SAM is independent of the number of spectral bands and insensitive to sunlight.…”

Section: The Proposed Methodologymentioning

confidence: 99%

“…In the unsupervised machine learning paradigm (Hussain et al, 2013;Bruzzone & Prieto, 2000), changes are commonly detected by resorting to the CVA strategy that computes a measure of similarity (or distance) between co-located pixels of a couple of images and uses a threshold-based approach to identify a distance threshold to separate changed pixels from the unchanged background. Various similarity (or distance) measures have been investigated for CVA methods (e.g., Appice et al, 2020;Falini et al, 2020;Seydi & Hasanlou, 2017;Yang & Mueller, 2007). The threshold to detect the changes is estimated by resorting to the spectral data (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…In (López-Fandiño et al, 2019), Otsu's algorithms is evaluated in combination with SAM and Watershed algorithm. Alternatively, clustering algorithms are adopted Appice et al, 2020), in order to separate distances (or similarities) of changed pixels from the unchanged background.…”

Section: Related Workmentioning

confidence: 99%

“…SAM, Z-score Information Divergence) computed between the spectral vectors on corresponding pixels (Choi et al, 2010). A recent study (Appice et al, 2020) adopts a spectral-spatial distance for CVA. In addition, it introduces an iterative upgrade of the traditional distance-based approach by accounting for a representation of the possible change iteratively learned through classification.…”

Section: Related Workmentioning

confidence: 99%

“…The neighbourhood (u, v) is a set of pixels surrounding (u, v) in the study scene. As in (Appice et al, , 2020Guccione et al, 2015), we consider a Fig. 3 Majella Park bi-temporal scene: the separation of the scene into black changed pixels and white unchanged background, which is computed via the Otsu's algorithm (Fig.…”

Section: I=1mentioning

confidence: 99%

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Leveraging autoencoders in change vector analysis of optical satellite images

et al. 2021

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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).

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Section: The Proposed Methodologymentioning

confidence: 99%