Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data

Faran, Ido; Netanyahu, Nathan S.; David, Eli; Shoshany, Maxim; Kizel, Fadi; Chang, Jisung Geba; Rud, Ronit

doi:10.1109/igarss.2019.8900186

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, local knowledge is needed to create a mapping between the datasets classes. Moreover, our approach does not depend on the definition of endmembers sets as in (Faran et al, 2019). When compared to the use of UAVs, our approach is cheaper to implement since we use a freely available alternative ground truth dataset, and a modest size but high-fidelity ground-truth dataset to calibrate IF parameters where part of the dataset was created using Open Street Maps volunteered data.…”

Section: Discussionmentioning

confidence: 99%

“…However, the need for domain experts to define the labelling functions represents a limitation. Other authors have explored the use of Vectorized Code Projected Gradient Descent Unmixing (VPGDU) (Faran et al, 2019) to simulate the ground truth of mid-resolution data by applying unmixing techniques to highresolution hyperspectral images. However, the selection of relevant endmembers sets is a key issue in achieving successful unmixing (Kizel & Shoshany, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing Data Layouts to Bring Storage Engine Functionality to Blockchain Technology

Przytarski,

Stach,

Mitschang

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modestsize, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative groundtruth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Data Layouts to Bring Storage Engine Functionality to Blockchain Technology

Przytarski,

Stach,

Mitschang

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Regarding the BRDF effect, given an image acquired under a particular geometrical configuration (Roy et al, 2016), a pixel's reflectance still depends on its spatial location within the scene. This dependency negatively affects the results of essential remote sensing tasks, e.g., classification (Faran et al, 2019;Melgani and Bruzzone, 2004), spectral unmixing (Kizel and Benediktsson, 2020) (Keshava and Mustard, 2002), and change detection (Eismann et al, 2008). Reducing this dependency is critical; therefore, a BRDF correction is usually applied to modulate the undesired trend on the data that occurs due to this effect and then subtract it from the measurements.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Reflectance Distribution Function (Brdf) of Mixed Pixels

Kizel

Vidro

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Hyperspectral imaging is crucial for a variety of land-cover mapping and analyzing tasks. The available large number of reflected light measurements along a wide range of wavelengths allows for distinguishing between different materials under various conditions. Though, several effects bear an undesired variability within hyperspectral images and increase the complexity of interpreting such data. Two of the most significant effects in this regard are the BRDF and the spectral mixture. Due to the first, the acquisitions geometrical and viewing conditions influences the measured spectral signature of a surface to a large extent. On the other hand, because of the typical low spatial resolution of remotely sensed images, each pixel can contain more than one material. Despite much research addressing either the BRDF effect and ways to correct it or the spectral unmixing, too few works considered these two effects' mutual influence. In this work, we study the BRDF of mixed pixels and present preliminary insights of testing a strategy to correct its undesired impact on the data by incorporating the EMs fractions within an unmixing-based semi-empirical correction model. Experimental results using real laboratory data acquired under controlled conditions clearly show the significant improvement of the corrected reflectance results through the proposed model.

show abstract