Small Woody Features (SWF) represent some of the most stable vegetated linear and small landscape features providing numerous ecological and socio-cultural functions, which can be grouped in four main categories: soil and water conservation, climate protection and adaptation, support to biological diversity, and cultural identity. Copernicus Land monitoring service, through the High-Resolution Layers, aims to map those SWFs at Pan- European level (39 countries, 6 million square kilometers) with the use of more than 37,000 Very High Spatial Resolution (VHSR) Earth Observation (EO) scenes. This unprecedented mapping exercise is focused on the extraction of SWF with a maximum width of 30m and a minimum length of 50m for linear features and a minimum and maximum area of 200 and 5,000m respectively. The main outputs are vector and raster products from the Pan-European coverage of the VHSR image data available from the European Space Agency (ESA) Copernicus Space Component Data Access (CSCDA) VHR IMAGE 2015 dataset. To fulfill this goal with a semi-automated approach, we developed a classification processing chain with the goal of very high computer efficiency and accuracy, using Object Based Image Analysis (OBIA) approach and Cloud-computing solutions. This highly efficient methodology based on differential attribute profiles (DAP) and classical classifier such as Random-Forest is particularly adapted to this exercise since it combines the use of spatial information and the spectral signature of each pixel. In this paper, we present the detailed methodology validated at Pan-European scale with various VHSR data source and landscape characteristics as well as full production results and internal validation.
Recent advances in very high resolution (VHR) earth observation (EO) techniques have led to a massive increase in data volumes to be processed. These remote sensing (RS) processing steps are complex and heterogeneous and an optimized use of these algorithms in both High Processing Computer (HPC) and Cloud platforms are still an important and opened study field for RS data providers with actual and future EO missions. The goal of our study is to identify and develop a new architecture to deal with large and increasing quantity of data and versatile production profiles. In this study, we took the example of the actual and complete processing pipeline used in Pleiades production to deliver perfect sensor image. This pipeline is composed of heterogeneous radiometric and geometric processing steps. In the first part, we study five main big data framework solutions. As result of this study, we identify Apache Spark as the best framework to use due to its performance, great development maturity, and data resilience certification. In the second part, and to develop the new processing pipeline, we redesign the processing pipeline with separation of the metadata management and the core processing. These good practices help us to develop and reuse legacy algorithms to an operational processing pipeline compatible with big data paradigm. As result of this development, we successfully identify a generic way to develop new processes and reuse legacy algorithms with large data paradigm and by keeping great performance and, more importantly, gaining platform flexibility.
Restoration of Very High Resolution (VHR) optical Remote Sensing Image (RSI) is critical and leads to the problem of removing instrumental noise while keeping integrity of relevant information. Improving denoising in an image processing chain implies increasing image quality and improving performance of all following tasks operated by experts (photo-interpretation, cartography, etc.) or by algorithms (land cover mapping, change detection, 3D reconstruction, etc.). In a context of large industrial VHR image production, the selected denoising method should optimized accuracy and robustness with relevant information and saliency conservation, and rapidity due to the huge amount of data acquired and/or archived. Very recent research in image processing leads to a fast and accurate algorithm called Non Local Bayes (NLB) that we propose to adapt and optimize for VHR RSIs. This method is well suited for mass production thanks to its best trade-off between accuracy and computational complexity compared to other state-of-the-art methods. NLB is based on a simple principle: similar structures in an image have similar noise distribution and thus can be denoised with the same noise estimation. In this paper, we describe in details algorithm operations and performances, and analyze parameter sensibilities on various typical real areas observed in VHR RSIs.
As acquisition technology progresses, remote sensing data contains an ever increasing amount of information: optical
and radar images, low, high and very high-resolution, multitemporal hyperspectral images, derived images, and physical
or ancillary data (databases, Digital Elevation Model (D.E.M), Geographical Information System (G.I.S.)). Future
projects in remote sensing will give high repeatability of acquisition like Venμs (CNES) which may provide data every 2
days with a resolution of 5.3 meters on 12 bands (420nm-900nm) and Sentinel-2 (ESA) 13 bands, 10-60m resolution and
5 days. With such data, supervised classification gives excellent results in term of accuracy indices (like Overall
Accuracy, Kappa coefficient). In this paper, we present advantages and disadvantages of existing indices and propose a
new index to evaluate supervised classification using all the information available from the confusion matrix. In addition
to accuracy, a new feature is introduced in this index: fidelity. For example, a class could have a high accuracy (low
omission error) but could be over-represented with other classes (high commission error). The new index reflects
accuracy and correct representation of classes (fidelity) using commission and omission errors. Environment applications
are in land cover and land use and the goal is to have the best classification for all classes, whether the biggest (corn,
trees) or the lightest (rivers, hedges). The tests are performed on Formosat-2 images (every 2 days, 8 meters resolution
on 4 bands) in the area of Toulouse (France). Tests used to validate the new index by demonstrating benefits of its use
through various thematical studies.
A methodology to highlight changes in the landscape based on satellite image classification has been developed
involving unsupervised and supervised approaches.
With past acquisitions, ground truth data are in general not known, therefore the classification can only be unsupervised.
These classifications provide labels but not surface types. The main difficulty lies in the interpretation of these classes.
An automatic interpretation method has been developed to allocate semantics to classes thanks to a radiometric value
catalogue. However, it requires radiometrically comparable images. After radiometric correction, the images are not free
from defects; this is why a normalization method has been developed.
We propose a specific methodology to evaluate changes consisting in regrouping classes of the same theme, smoothing
and eroding contours without taking "mixels" into account and comparing the classified images to provide statistics and
image changes. The different steps of the process are essential to avoid false changes and to quantify land cover change
with a high degree of accuracy. Various statistical results are given: changes or no changes, types of changes, and crop
rotations over several years.
Land use /cover change (LUCC) can provide an estimate of carbon capture and storage. Reforestation, changing land use
and best practices can increase carbon sequestration in biomass and soils for a period of several decades, which may
constitute a significant contribution to the fight against the greenhouse effect. Deforestation, conversely, can lead to
significant levels of CO2 emission.
By application to the South-West region of Toulouse, we observe significant land cover changes over 11 years (1991-
2002). The crop rotations are given for 4 years (year per year 2002-2005).
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