Advanced Classification of Remote Sensing High Resolution Imagery. An Application for the Management of Natural Resources

Ibarrola-Ulzurrun, Edurne; Marcello, Javier; Gonzalo-Martín, Consuelo

doi:10.1007/978-3-319-58965-7_1

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…With the development of remote sensing sensor technology such as WorldView 4 (WV-4), GaoFen-2, SuperView-1 etc., the spatial resolution of remote sensing images has been increasing year by year. More and more very high resolution images (VHRI) have been obtained and widely used in many fields, such as in natural sources management [1][2][3], urban mapping [4] and military reconnaissance. VHRI contains extremely rich information, according to which some simple features can be extracted and selected as basic features to classify categories in VHRI.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification

Ming

et al. 2018

Remote Sensing

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Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.

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Section: Introductionmentioning

confidence: 99%

A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification

Ming

et al. 2018

Remote Sensing

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“…Remote sensors provide raw data images; thus, it is necessary to apply correction techniques and to perform image pre‐processing in order to obtain high‐quality imagery (Ibarrola‐Ulzurrun et al. ).…”

Section: Methodsmentioning

confidence: 99%

Temporal dynamic analysis of a mountain ecosystem based on multi‐source and multi‐scale remote sensing data

et al. 2019

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Citation: Ibarrola-Ulzurrun, E., J. Marcello, C. Gonzalo-Mart ın, and J. L. Mart ın-Esquivel. 2019. Temporal dynamic analysis of a mountain ecosystem based on multi-source and multi-scale remote sensing data. Ecosphere 10(6):Abstract. During the last decades, ecosystems have suffered a decline in natural resources due to climate change and anthropogenic pressure. Specifically, the European rabbit introduced by humans, as well as drought episodes, have led to a change in the vegetation structure of a mountainous ecosystem: Teide National Park in Spain. Teide managers studied, with field-based traditional methods, how the two keystone vegetation species, Spartocytisus supranubius and Pterocephalus lasiospermus, have changed their dynamics in this vulnerable and heterogenic ecosystem. However, remote sensing is an important tool for classifying, monitoring, and managing large areas in a fast and economic way. This work proposes a methodological framework to monitor the changes produced in this protected area using multi-source remote sensing imagery. The results strengthen and extend the analysis followed by the National Park managers, demonstrating that S. supranubius has decreased its population while P. lasiospermus has increased. Moreover, this study presents thematic maps of the species of interest, as well as its specific coverage at different dates, providing quantitative data difficult to get with traditional approaches.

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Pansharpening Based on Deconvolution for Multiband Filter Estimation

Vivone

Addesso

Restaino

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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Advanced Classification of Remote Sensing High Resolution Imagery. An Application for the Management of Natural Resources

Cited by 3 publications

References 17 publications

A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification

A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification

Temporal dynamic analysis of a mountain ecosystem based on multi‐source and multi‐scale remote sensing data

Pansharpening Based on Deconvolution for Multiband Filter Estimation

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