Dr. Claudia Paris Profile

Dr. Claudia Paris

Assistant Professor at University of Twente

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 19 October 2023 Presentation + Paper

Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data

Claudia Paris, Laura Martinez-Sanchez, Marijn van der Velde, Surbhi Sharma, Rocco Sedona, Gabriele Cavallaro

Proceedings Volume 12733, 127330M (2023) https://doi.org/10.1117/12.2679433

KEYWORDS: Satellites, Semantics, Image segmentation, Satellite imaging, Statistical analysis, Spatial resolution, Databases, Classification systems, Vegetation, Education and training

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Proceedings Article | 19 October 2023 Poster + Paper

A study on the impact of the spatial and spectral resolution on plant species richness in Mediterranean regions using optical remote sensing data

Ama Serwah Boakye, Margarita Huesca Martinez, Claudia Paris

Proceedings Volume 12733, 127330W (2023) https://doi.org/10.1117/12.2679116

KEYWORDS: Remote sensing, Earth observing sensors, Landsat, Spatial resolution, Spectral resolution, Biodiversity, Vegetation, Data acquisition, Hyperspectral imaging, Biological research

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Plant Species Richness (PSR) is one of the most widely used metrics to estimate alpha diversity in ecology. Several approaches have been developed to estimate PSR with Remote Sensing (RS) data. Among them, the Spectral Diversity Hypothesis (SDH) approach can be successfully applied to airborne hyperspectral data. Although effective, these data are limited in space and time due to high aerial acquisition costs. Satellite multispectral data are continuously acquired on a global scale, but their spatial and spectral resolutions are not comparable to those of hyperspectral data. Although some studies compared different optical data for estimating PSR using SDH, the impact of the spatial and spectral resolutions on the assessment of this biodiversity indicator is not clear. Moreover, most of the studies focus on dense tropical forest areas or wetlands, while little has been done to test the SDH approach in open forests located in Mediterranean regions. For all these reasons, the present work aims to: (1) apply and interpret PSR estimated with the SDH approach in open Mediterranean forest, and (2) evaluate the impact of the spatial and spectral resolutions on PSR estimation using real and simulated RS data. The PSR was estimated applying the SDH approach on a 4m hyperspectral data (373 bands), 30m multispectral satellite data (7 bands), synthetic 16m and 30m hyperspectral data (373 bands), and synthetic 4m multispectral data (7 bands). Preliminary results carried out in the San Joaquin Experimental Range (SJER) indicate that: (1) there is a weak correlation between spectral and species diversity in the less dense forest areas (R² =-0.13 for the hyperspectral and R² =0.14 for the multispectral data), while revealing good correlation in the more dense forest areas (R² =0.68 for the hyperspectral and R² =0.65 for the multispectral data), (2) the number of identified spectral species is more influenced by the spectral resolution than the spatial one, and (3) high spatial resolution data tends to overestimate the PSR in less dense forest areas because of the influence of background and understory vegetation.

Proceedings Article | 12 September 2021 Presentation + Paper

Multi-year crop type mapping using pre-trained deep long-short term memory and Sentinel 2 image time series

Giulio Weikmann, Claudia Paris, Lorenzo Bruzzone

Proceedings Volume 11862, 118620O (2021) https://doi.org/10.1117/12.2600559

KEYWORDS: Composites, Remote sensing, Network architectures, Image classification, Clouds

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Proceedings Article | 21 September 2020 Presentation + Paper

Monitoring of agricultural areas by using Sentinel 2 image time series and deep learning techniques

Claudia Paris, Giulio Weikmann, Lorenzo Bruzzone

Proceedings Volume 11533, 115330K (2020) https://doi.org/10.1117/12.2574745

KEYWORDS: Agriculture, Multispectral imaging, Convolutional neural networks, Satellites, Remote sensing, Data modeling, Satellite imaging, Earth observing sensors, Earth sciences, Image classification

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Proceedings Article | 21 September 2020 Poster + Presentation + Paper

A study of the robustness of the long short-term memory classifier to cloudy time series of multispectral images

Iwona Podsiadlo, Claudia Paris, Lorenzo Bruzzone

Proceedings Volume 11533, 115331O (2020) https://doi.org/10.1117/12.2574383

KEYWORDS: Clouds, Image classification, Multispectral imaging, Remote sensing, Earth sciences, Earth observing sensors, Landsat, Lithium, Neural networks, Satellite imaging

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Sentinel-2 and Landsat satellites provide huge amount of optical images with high spatial and temporal resolution. These dense Time Series (TS) of multispectral data are used for a wide range of applications enabling multi-temporal monitoring of physical phenomena. Nevertheless, one of the main challenges in their usage is related to missing information caused by cloud occlusions. In the literature, many cloud restoration approaches have been proposed. However, to properly recover missing information, sophisticated and usually computationally intensive techniques should be used. In this work, we consider the deep Long Short Term Memory (LSTM) classifier which is very promising for classification of dense time series of images, and investigate its robustness to the cloud presence without any cloud restoration. Indeed, this classifier has proven to be able to handle the presence of clouds. However, no work which extensively analyzes the robustness of LSTM to clouds can be found in the literature. In this study, we aim to quantitatively asses the capability of the network of handling different amount of cloud coverage under different lengths of the TS. In greater detail, we analyze the effect of the cloud coverage on the classification maps produced by the LSTM by considering: (i) simulated cloud values, (ii) detected clouds represented by zeros values, and (iii) restored images by simple linear temporal gap filling (i.e., average of the spectral values acquired in the previous and following cloud-free images in the TS). The obtained results demonstrate that the capability of the LSTM to handle the cloud cover depends on: (i) the length of the TS, (ii) the position of the cloudy images in the TS, and (iii) the cloud representation values. For example, when clouds are restored with very simple and fast linear temporal gap filling, the map agreement between the cloud-free and the cloudy map is 96% even when the 40% of images in the TS are covered with clouds, regardless of their position.

Proceedings Article | 7 October 2019 Presentation + Paper

Integration of hydro-climatological model and remote sensing for glacier mass balance estimation

Iwona Podsiadlo, Claudia Paris, Francesca Bovolo, Mattia Callegari, Ludovica De Gregorio, Daniel Günther, Carlo Marin, Thomas Marke, Milad Niroumand-Jadidi, Claudia Notarnicola, Ulrich Strasser, Marc Zebisch, Lorenzo Bruzzone

Proceedings Volume 11155, 1115513 (2019) https://doi.org/10.1117/12.2533232

KEYWORDS: Remote sensing, Data modeling, Numerical simulations, Multispectral imaging, Climatology, Meteorology, Statistical modeling, Statistical analysis

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