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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Impervious Surface Extraction from Hyperspectral Images via Superpixels Based Sparse Representation with Morphological Attributes Profiles

verfasst von : Jun Rong, Genyun Sun, Aizhu Zhang, Hui Huang

Erschienen in: Advances in Brain Inspired Cognitive Systems

Verlag: Springer International Publishing

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Abstract

Impervious surface is an important factor in monitoring urban development and environmental analysis. However, spectral differences and structural differences exist on impervious surfaces, which leads to accurate extraction of impervious surfaces is a difficult task. Therefore, this paper proposes a superpixel sparse representation based on morphological profiles and raw data to extract the impervious surface on hyperspectral imagery. Specifically, the segmentation map of the image is first generated by the mean shift segmentation. Then, the morphological attribute profiles of the hyperspectral image are extracted and stacked with raw data. Finally, the segmentation map is masked onto the stacked image and each resulting superpixel is classified via sparse representation. Experiments show that the method has good performance in impervious surface extraction and has advantages in the comparison method. This shows the effectiveness of the proposed method.

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Vorheriges Kapitel Action Recognition in Videos with Temporal Segments Fusions
Nächstes Kapitel Dilated Convolutional Network for Road Extraction in Remote Sensing Images
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Metadaten
Titel
Impervious Surface Extraction from Hyperspectral Images via Superpixels Based Sparse Representation with Morphological Attributes Profiles
verfasst von
Jun Rong
Genyun Sun
Aizhu Zhang
Hui Huang
Copyright-Jahr
2020
Buch
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-39430-1

Electronic ISBN: 978-3-030-39431-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39431-8_24