Unsupervised deep learning of landscape typologies from remote sensing images and other continuous spatial data

Strien, Maarten J. van; Grêt‐Regamey, Adrienne

doi:10.1016/j.envsoft.2022.105462

Cited by 12 publications

(2 citation statements)

References 57 publications

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“…Firstly, the DCEC model combines the power of deep convolutional autoencoders, which are neural networks used for dimensionality reduction, with clustering algorithms to perform joint feature learning and clustering simultaneously [24,25]. This model has shown promising results in various image-related tasks, such as object recognition and segmentation, remote sensing image classification, data dimensionality reduction, and image noise reduction [12,[26][27][28]. In comparison to traditional feature extraction methods, the high-level features extracted by the DCEC model provide better amplitude and phase information.…”

Section: The Effectiveness Of the Model In Extracting Sst Featuresmentioning

confidence: 99%

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

Zhang,

Xin,

et al. 2024

Fishes

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We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (FM) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted FM to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with FM outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both FM and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models.

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Section: The Effectiveness Of the Model In Extracting Sst Featuresmentioning

confidence: 99%

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

Zhang,

Xin,

et al. 2024

Fishes

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show abstract

“…Compared with GIS, the use of programming languages for image processing presents new possibilities in mapping through the extended functionality of algorithms and has received much attention in the geoscience community [61][62][63][64][65]. As previous work shows [66][67][68], integrating spatial information from the satellite images using various perceptual tasks, such as object recognition, location, classification and interpretation, are necessary for disambiguating visually similar land cover classes of the Earth's surface representing the landscapes.…”

Section: Introductionmentioning

confidence: 99%

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

Lemenkova

Debeir

2022

Applied Sciences

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In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

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Machine Learning Techniques for the Semiautomated Recognition of Urban and Peri-Urban Configurations

Dato

Falasca

Marucci

2023

Lecture Notes in Computer Science

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Unsupervised deep learning of landscape typologies from remote sensing images and other continuous spatial data

Cited by 12 publications

References 57 publications

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

Machine Learning Techniques for the Semiautomated Recognition of Urban and Peri-Urban Configurations

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