AN OPTIMISED SYSTEM FOR GENERATING MULTI-RESOLUTION DTMS USING NASA MRO DATASETS

Within the EU FP-7 iMars project, a fully automated multi-resolution DTM processing chain, called Co-registration ASP-Gotcha Optimised (CASP-GO) has been developed, based on the open source NASA Ames Stereo Pipeline (ASP). CASP-GO includes tiepoint based multi-resolution image co-registration and an adaptive least squares correlation-based sub-pixel refinement method called Gotcha. The implemented system guarantees global geo-referencing compliance with respect to HRSC (and thence to MOLA), provides refined stereo matching completeness and accuracy based on the ASP normalised cross-correlation. We summarise issues discovered from experimenting with the use of the open-source ASP DTM processing chain and introduce our new working solutions. These issues include global co-registration accuracy, de-noising, dealing with failure in matching, matching confidence estimation, outlier definition and rejection scheme, various DTM artefacts, uncertainty estimation, and quality-efficiency trade-offs.

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UNCERTAINTY ESTIMATION FOR END-TO-END LEARNED DENSE STEREO MATCHING VIA PROBABILISTIC DEEP LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-161-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 161-169

Author(s):

M. Mehltretter

Keyword(s):

Neural Network ◽

Deep Learning ◽

Stereo Matching ◽

Probabilistic Neural Network ◽

Uncertainty Estimation ◽

Training Data ◽

Stereo Image ◽

Confidence Estimation ◽

Extensive Evaluation ◽

Dense Stereo Matching

Abstract. Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however, is particularly beneficial if the domain of the training data varies from that of the data to be processed. For this purpose, in the present work the idea of probabilistic deep learning is applied to the task of dense stereo matching for the first time. Based on the well-known and commonly employed GC-Net architecture, a novel probabilistic neural network is presented, for the task of joint depth and uncertainty estimation from epipolar rectified stereo image pairs. Instead of learning the network parameters directly, the proposed probabilistic neural network learns a probability distribution from which parameters are sampled for every prediction. The variations between multiple such predictions on the same image pair allow to approximate the model uncertainty. The quality of the estimated depth and uncertainty information is assessed in an extensive evaluation on three different datasets.

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A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

Remote Sensing ◽

10.3390/rs13101903 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1903

Author(s):

Zhihui Li ◽

Jiaxin Liu ◽

Yang Yang ◽

Jing Zhang

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Stereo Matching ◽

Mean Shift ◽

Segmentation Method ◽

Remote Sensing Images ◽

Edge Matching ◽

Refinement Method ◽

Initial Segmentation ◽

Refinement Algorithm

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

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The evolution of life cycle assessment in European policies over three decades

The International Journal of Life Cycle Assessment ◽

10.1007/s11367-021-01893-2 ◽

2021 ◽

Author(s):

Serenella Sala ◽

Andrea Martino Amadei ◽

Antoine Beylot ◽

Fulvio Ardente

Keyword(s):

Life Cycle Assessment ◽

Life Cycle ◽

Decision Support ◽

General Concept ◽

Life Cycle Thinking ◽

Environmental Footprint ◽

Science And Policy ◽

Policy Domain ◽

Trade Offs ◽

The Eu

Abstract Purpose Life cycle thinking (LCT) and life cycle assessment (LCA) are increasingly considered pivotal concept and method for supporting sustainable transitions. LCA plays a relevant role in decision support, for the ambition of a holistic coverage of environmental dimensions and for the identification of hotspots, possible trade-offs, and burden shifting among life cycle stages or impact categories. These features are also relevant when the decision support is needed in policy domain. With a focus on EU policies, the present study explores the evolution and implementation of life cycle concepts and approaches over three decades. Methods Adopting an historical perspective, a review of current European Union (EU) legal acts and communications explicitly mentioning LCT, LCA, life cycle costing (LCC), and environmental footprint (the European Product and Organisation Environmental Footprint PEF/OEF) is performed, considering the timeframe from 1990 to 2020. The documents are categorised by year and according to their types (e.g. regulations, directives, communications) and based on the covered sectors (e.g. waste, energy, buildings). Documents for which life cycle concepts and approaches had a crucial role are identified, and a shortlist of these legal acts and communications is derived. Results and discussion Over the years, LCT and life cycle approaches have been increasingly mentioned in policy. From the Ecolabel Regulation of 1992, to the Green Deal in 2019, life cycle considerations are of particular interest in the EU. The present work analysed a total of 159 policies and 167 communications. While in some sectors (e.g. products, vehicles, and waste) life cycle concepts and approaches have been adopted with higher levels of prescriptiveness, implementation in other sectors (e.g. food and agriculture) is only at a preliminary stage. Moreover, life cycle (especially LCT) is frequently addressed and cited only as a general concept and in a rather generic manner. Additionally, more stringent and rigorous methods (LCA, PEF/OEF) are commonly cited only in view of future policy developments, even if a more mature interest in lifecycle is evident in recent policies. Conclusion The EU has been a frontrunner in the implementation of LCT/LCA in policies. However, despite a growing trend in this implementation, the development of new stringent and mandatory requirements related to life cycle is still relatively limited. In fact, there are still issues to be solved in the interface between science and policy making (such as verification and market surveillance) to ensure a wider implementation of LCT and LCA.

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