One of the main concerns in adopting citizen science is data quality. Derived products inherit intrinsic limitations of the capture methodology as well as the uncertainties in observations. OpenStreetMap tools are designed to minimize uncertainties in positional accuracy by ensuring a good co-registration of the observations with imagery or direct use of GPS. When thematically annotating objects contributed by citizens, uncertainty increases. During the H2020 GroundTruth 2.0 project two land-cover products derived from OSM were analyzed; one created by the University of Heidelberg (http://osmlanduse.org) and another elaborated by University of Coimbra (https://vgi.uc.pt/vgi/osm/osm2lulc/). To be able to assess the quality of both maps, a third product derived from remote sensing was introduced as a reference map. In GroundTruth 2.0 a tool to show and compare maps as part of the MiraMon Map Browser was developed. The objective was to allow final users to auto-evaluate the quality of their region of interest. The confusion matrix has been used as a method to derive overall commission and omission estimators as well as the Kappa coefficient. Most of the discrepancies between OSM and remote sensing (RS) derived maps are related to different approaches used during data capturing. The data quality tool assesses the quality of individual observations exposed using the OGC standard and describes the quality in an interoperable approach based on QualityML.
This work aims to determine an efficient procedure (balanced between quality and compression ratio) for compressing
multispectral remote sensing time series images in a 4-dimensional domain (2 spatial, 1 spectral and 1 temporal
dimension). The main factors studied were: spectral and temporal aggregation, landscape type, compression ratio, cloud
cover, thermal segregation and nodata regions.
In this study, the authors used three-dimensional Discrete Wavelet Transform (3d-DWT) as the compression
methodology, implemented in the Kakadu software with the JPEG2000 standard. This methodology was applied to a
series of 2008 Landsat-5 TM images that covered three different landscapes, and to one scene (19-06-2007) from a
hyperspectral CASI sensor.
The results show that 3d-DWT significantly improves the quality of the results for the hyperspectral images; for
example, it obtains the same quality as independently compressed images at a double compression ratio. The differences
between the two compression methodologies are smaller in the Landsat spectral analysis than in the CASI analysis, and
the results are more irregular depending on the factor analyzed. The time dimensional analysis for the Landsat series
images shows that 3d-DWT does not improve on band-independent compression.
The aim of this work is to, within the JPEG2000 framework, enhance the coding performance obtained for images that contain regions without useful information, or without information at all, here named as NODATA regions. In Geographic Information Systems (GIS) and in Remote Sensing (RS), NODATA regions arise due to several factors, such as geometric and radiometric corrections, atmospheric events, the overlapping of successive layers of information, etc. Most coding systems are not devised to consider these regions separately from the rest of the image, sometimes causing a loss in the coding efficiency and in the post-processing applications. We propose two approaches that address this issue; the first technique (Average Data Region, ADR) is carried out as simple pre-processing and the second technique (Shape-Adaptive JPEG2000, SA-JPEG2000) modifies the coding system to avoid the regions without information. Experimental results, performed on data from real applications and different scenarios, suggest that the proposed approaches can achieve, e.g., for SA-JPEG2000, a Signal-to- Noise Ratio improvement of about 8 dB. Moreover, in a post-processing application such as a digital classification, the best classification results are obtained when the proposed approaches SA-JPEG2000 and ADR are applied.
This study deals with the effects of lossy image compression in the visual analysis of remotely sensed images. The experiments consider two factors with interaction: the type of landscape and the degree of lossy compression. Three landscapes and two areas for each landscape (with different homogeneity) have been selected. For every of the six study area, color 1:5000 orthoimages have been submitted to a JPEG2000 lossy compression algorithm at five different compression ratios. The image of every area and compression ratio has been submitted to on-screen photographic interpretation, generating 30 polygon layers. Maps obtained using compressed images with a high compression ratio present high structural differences regarding to maps obtained with the original images. On the other hand, the
compression of 20% obtains values only slightly different from those of the original photographic interpretation, but these differences seem owed to the subjectivity of the photographic interpretation. Therefore, this compression ratio seems to be the optimum since it implies an important reduction of the image size without determining changes neither in the topological variables of the generated vector nor in the obtained thematic quality.
The size of images used in remote sensing scenarios has constantly increased in the last years. Remote sensing images
are not only stored, but also processed and transmitted, raising the need for more resources and bandwidth. On another
side, hyperspectral remote sensing images have a large number of components with a significant inter-component redundancy,
which is usually taken into account by many image coding systems to improve the coding performance. The
main approaches used to decorrelate the spectral dimension are the Karhunen Loeve-Transform and the Discrete Wavelet
This paper is focused on DWT decorrelators because they have a lower computational complexity, and because they
provide interesting features such as component and resolution scalability and progressive transmission. Influence of the
spectral transform is investigated, considering the DWT kernel applied and the number of decomposition levels.
In addition, a JPIP compliant application, CADI, is introduced. It may be useful to test new protocols, techniques, or
coding systems, without requiring significant changes on the application. CADI can be run in most computer platforms and
devices thanks to the use of JAVA and the configuration of a light-version, suitable for devices with constrained resources.
Multiple regression is a common technique used when performing digital analysis on images to obtain continuous,
quantitative, variables (as temperature, biomass, etc). In this scenario pixels are treated as samples from which several
independent variables are known; when remote sensing images are available, the different spectral bands offered by a
given sensor are often used as independent variables. The dependent variable is also a quantitative variable, such as a
forest inventory variable or a climate variable (e.g., temperature). This paper presents an evaluation of the implications
of JPEG2000 lossy compression when applied to these regression processes. Annual minimum and annual mean air
temperature are modelled using two methods according to the independent variables used: only geographical, and
geographical and remote sensing images as independent variables. Raster matrix representing independent variables were
compressed using compression ratios from 50% up to 0.01% of the original file size. Results have revealed that, even at
high compression ratios, practically the same accuracy, measured with independent reference climatic stations, is
obtained, so JPEG2000 seems an interesting technique not heavily affecting these common modelling approaches.
In 2005, the Consultative Committee for Space Data Systems (CCSDS) approved a new Recommendation (CCSDS 122.0-B-1) for Image Data Compression. Our group has designed a new file syntax for the Recommendation. The proposal
consists of adding embedded headers. Such modification provides scalability by quality, spatial location, resolution and
component. The main advantages of our proposal are: 1) the definition of multiple types of progression order, which enhances
abilities in transmission scenarios, and 2) the support for the extraction and decoding of specific windows of interest
without needing to decode the complete code-stream. In this paper we evaluate the performance of our proposal. First we
measure the impact of the embedded headers in the encoded stream. Second we compare the compression performance of
our technique to JPEG2000.