Feature space dimensionality reduction for the optimization of visualization methods

Griparis, Andreea; Faur, Daniela; Datcu, Mihai

doi:10.1109/igarss.2015.7325967

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature extraction stage aims to describe the content of each patch through the use of specific descriptors. For this study we have used a concatenated feature vector meaning that we have included the spectral signature, texture and the Weber Local Descriptors [7]. The derived multidimensional space was projected in the IVI using three dimensionality reduction methods: Principal Component Analysis, Stochastic Proximity Embedding and t-Distributed Stochastic Neighbor Embedding [6] [7].…”

Section: Results and Conclusionmentioning

confidence: 99%

“…(a) Sentinel-2 scene revealing a forestred area at 04.2018; (b) Corine Landcover classes overlapping the scene, supporting the user in the selection of the positive (green) and negative (red) samples for the training class; the projection of the multidimensional space of the feature vectors using Stochastic proximity embedding alogorithm (c)[6] and Principal Component Analysis algorithm (d)[7].…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Interactive Visual Analytics Tool for Big Earth Observation Data Content Estimation

Faur

Griparis

Stoica³

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

View full text Add to dashboard Cite

This paper introduces a tool designed to provide an innovative and insightful way of exploring Earth observation data content beyond visualization, by addressing a visual analytics process. The considered framework combines machine learning and visualization techniques, empowered through human interaction, to gain knowledge from the data. The proposed tool-eVADE leverages the methodologies developed in the fields of information retrieval, data mining and knowledge representation by the means of a visual analytics component. eVADE increases users capability to understand and extract meaningful semantic clusters together with quantitative measurements, presented in a suggestive visual way.

show abstract

Section: Results and Conclusionmentioning

confidence: 99%

mentioning

confidence: 99%

An Interactive Visual Analytics Tool for Big Earth Observation Data Content Estimation

Faur

Griparis

Stoica³

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

View full text Add to dashboard Cite

show abstract

“…They envisage multilayers transformation based on various rules to weight down the number of features by preserving the relevant information. Specific results for remote sensing data visualisation are presented in [20].…”

Section: Visual Data Analysismentioning

confidence: 99%

Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios

Grivei

Neagoe

Georgescu

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Sentinel-2 satellites provide systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of 5 days. Computer-based data analysis is highly required to extract similarity by processing and to assist human understanding and semantic annotation in support of mapping Earth's surface. This paper proposes a data mining concept that uses advanced data visualization and explainable features to enhance relevant aspects in the Sentinel-2 data and enable semantic analysis. There is a two-stage process. At first, spectral, texture, and physical parameters related features are extracted from the data and included in a learning process that models the data content according to statistical similarities. In parallel, the second processing stage maximizes the data impact on the human visual system to help image understanding and interpretation. Target classes are subject to exploratory visual analysis, such that both visual and latent characteristics are revealed to the user. The concept is further implemented as Sentinel-2 dedicated data analysis (DAS-Tool) plugin for the Sentinel Application Platform (SNAP) and deployed as an open-source tool empowering the Earth Observation (EO) community with fast and reliable results. Accommodating multiple solutions for each processing phase, the plugin enables flexibility in information extraction and knowledge discovery that will bring the best accuracy in mapping applications. For demonstration purposes, the authors focus on a detailed benchmark against reference data (ground truth) for the Southern region of Romania, then use the selected algorithms in a forest fires scenario analysis for the Sydney region in Australia. The processing involves full-size Sentinel-2 images.

show abstract

“…A comparative review of the recent developed non-linear DR methods was presented in [14]. The performance of three DR techniques in the visualization field of the remote sensing image dataset was studied in [15]. Principal Component Analysis, Linear Discriminant Analysis and t-distributed Stochastic Neighbour Embedding algorithms ware used to delineate an optimal image content visualization method.…”

Section: Review On Current Approachesmentioning

confidence: 99%

Exploratory search methodology for sentinel 2 data: A prospect of both visual and latent characteristics

Vaduva

Georgescu

Griparis

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

Sentinel 2 (S2) satellite provides a systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of 5 days. Computer-based data analysis is highly required to extract similarity by processing and assist human understanding and semantic annotation in support of Earth surface mapping. This paper proposes an exploratory search methodology for S2 data underpinning both visual and latent characteristics by means of data visualization and content representation. For optimized results, the authors focus on a detailed assessment of top relevant state-of-the-art algorithms for features extraction and classification to determine which one could handle best the characteristics of S2 data. Index Terms-exploratory multispectral data analysis Precision Recall Class Name 93.98% 87.54% Agriculture-HV 91.90% 82.41% Agriculture-LV 73.78% 89.52% Forest 86.49% 96.55% Urban 88.35% 81.98% Water

show abstract

Feature space dimensionality reduction for the optimization of visualization methods

Cited by 6 publications

References 6 publications

An Interactive Visual Analytics Tool for Big Earth Observation Data Content Estimation

An Interactive Visual Analytics Tool for Big Earth Observation Data Content Estimation

Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios

Exploratory search methodology for sentinel 2 data: A prospect of both visual and latent characteristics

Contact Info

Product

Resources

About