Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey

Savelonas, Michalis A.; Veinidis, Christos; Bartsokas, Theodoros K.

doi:10.3390/rs14236017

Cited by 12 publications

(8 citation statements)

References 338 publications

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“…Kent et al [18] used principal component analysis to find the most representative spatial daylight distribution patterns. Savelonas et al [19] provided an overview of computational methods aiding geoscientists in the analysis of 2D or 3D imaging data. Inspired by these models, we introduced a classifier to classify similar facial pose features as a classification.…”

Section: Facial Pose Transfermentioning

confidence: 99%

Facial Pose and Expression Transfer Based on Classification Features

et al. 2023

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Transferring facial pose and expression features from one face to another is a challenging problem and an interesting topic in pattern recognition, but is one of great importance with many applications. However, existing models usually learn to transfer pose and expression features with classification labels, which cannot hold all the differences in shape and size between conditional faces and source faces. To solve this problem, we propose a generative adversarial network model based on classification features for facial pose and facial expression transfer. We constructed a two-stage classifier to capture the high-dimensional classification features for each face first. Then, the proposed generation model attempts to transfer pose and expression features with classification features. In addition, we successfully combined two cost functions with different convergence speeds to learn pose and expression features. Compared to state-of-the-art models, the proposed model achieved leading scores for facial pose and expression transfer on two datasets.

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Section: Facial Pose Transfermentioning

confidence: 99%

Facial Pose and Expression Transfer Based on Classification Features

et al. 2023

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“…The Support Vector Machine (SVM) algorithm is one of the most traditional supervised ML classifier algorithms. The SVM classifier uses different planes in (n-dimensional) space to divide data points (pixels) (i.e., supposing two, linearly separable classes, the training objective is to determine a hyperplane in the feature space, which has the maximum distance from the nearest samples of both classes) [15]. Its performance and accuracy depend on selecting the hyperplane and the kernel parameter, which will produce a pixel classification model based on the training data or samples that can accurately predict class labels from the test data [3].…”

Section: Image Classifiers Based On ML Algorithmsmentioning

confidence: 99%

“…The ML Random Forest (RF) classifier is another algorithm widely used in pixel classification which is based on methods of generating an infinity of Decision Trees by joint learning and which evolves to a combination of these trees to obtain better ranking performance by averaging predictions [15,16]. Each Decision Tree is realized as a tree structure consisting of two types of nodes: decision and leaf nodes [15]. All trees are trained with the same parameters but using different sets of training samples.…”

Section: Image Classifiers Based On ML Algorithmsmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms in the Classification of Multispectral Images from the Sentinel-2A/2B Orbital Sensor for Mapping the Environmental Dynamics of Ria Formosa (Algarve, Portugal)

Souza,

Rodrigues

2023

IJGI

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With the growing availability of remote sensing orbital spatial data, the applications of machine learning (ML) algorithms have been leveraging the field of process automation in image classification. The present work aimed to evaluate the precision and accuracy of ML algorithms in the classification of Sentinel 2A/2B images from an area of high environmental dynamics, such as Ria Formosa (Algarve, Portugal). The images were submitted to classification by groups of ML algorithms such as the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). The Orfeo Toolbox (OTB) open-source programming package made the algorithms available. Ten samples were collected for each of the 14 land use and cover classes in the Ria Formosa area, totaling 140 samples. Of these, 70% were for training and 30% for validating the classification. The evaluation metrics used were the class discrimination measures: Recall (R), the Global Kappa Index (k), and the General Accuracy Index (OA). The results showed that the KNN and DT algorithms demonstrated a greater discrimination capacity for most classes (. SVM and RF significantly improved class discrimination when using larger samples for training. Merging the classified images significantly improved the classification accuracy, ranging from 71% to 81%. This evaluation made it possible to define sets of ML algorithms sensitive to change detection for mapping and monitoring dynamic environments.

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“…The key factors in effective approaches to satellite image processing include the resolution of data (e.g., 5-30 m/pixel for United States Geological Survey (USGS)/National Aeronautics and Space Administration (NASA) Landsat or high resolution with 1-5 m/pixel for Planet Labs Rapid Eye Satellite) and the functionality of the software [9]. Another important factor consists in the quality of image, coverage extent, cloudiness and haze.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

Lemenkova

2023

Land

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This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa.

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Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey

Cited by 12 publications

References 338 publications

Facial Pose and Expression Transfer Based on Classification Features

Facial Pose and Expression Transfer Based on Classification Features

Evaluation of Machine Learning Algorithms in the Classification of Multispectral Images from the Sentinel-2A/2B Orbital Sensor for Mapping the Environmental Dynamics of Ria Formosa (Algarve, Portugal)

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

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