Non-local tensor completion for multitemporal remotely sensed images inpainting

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Water ice in the dark dune spots of Richardson crater on Mars

In this study we assess the presence, nature and properties of ices - in particular water ice - that occur within these spots using HIRISE and CRISM observations, as well as the LMD Global Climate Model. Our studies focus on Richardson crater (72{\deg}S, 179{\deg}E) and cover southern spring and summer (LS 175{\deg} - 17 341{\deg}). Three units have been identified of these spots: dark core, gray ring and bright halo. Each unit show characteristic changes as the season progress. In winter, the whole area is covered by CO2 ice with H2O ice contamination. Dark spots form during late winter and early spring. During spring, the dark spots are located in a 10 cm thick depression compared to the surrounding bright ice-rich layer. They are spectrally characterized by weak CO2 ice signatures that probably result from spatial mixing of CO2 ice rich and ice free regions within pixels, and from mixing of surface signatures due to aerosols scattering. The bright halo shaped by winds shows stronger CO2 absorptions than the average ice covered terrain, which is consistent with a formation process involving CO2 re-condensation. According to spectral, morphological and modeling considerations, the gray ring is composed of a thin layer of a few tens of {\mu}m of water ice. Two sources/processes could participate to the enrichment of water ice in the gray ring unit: (i) water ice condensation at the surface in early fall (prior to the condensation of a CO2 rich winter layer) or during winter time (due to cold trapping of the CO2 layer); (ii) ejection of dust grains surrounded by water ice by the geyser activity responsible for the dark spot. In any case, water ice remains longer in the gray ring unit after the complete sublimation of the CO2. Finally, we also looked for liquid water in the near-IR CRISM spectra using linear unmixing modeling but found no conclusive evidence for it

Detection of Marine Plastic Debris in the North Pacific Ocean using Optical Satellite Imagery

Plastic pollution is ubiquitous across marine environments, yet detection of anthropogenic debris in the global oceans is in its infancy. Here, we exploit high-resolution multispectral satellite imagery over the North Pacific Ocean and information from GPS-tracked floating plastic conglomerates to explore the potential for detecting marine plastic debris via spaceborne remote sensing platforms. Through an innovative method of estimating material abundance in mixed pixels, combined with an inverse spectral unmixing calculation, a spectral signature of aggregated plastic litter was derived from an 8-band WorldView-2 image. By leveraging the spectral characteristics of marine plastic debris in a real environment, plastic detectability was demonstrated and evaluated utilising a Spectral Angle Mapper (SAM) classification, Mixture Tuned Matched Filtering (MTMF), the Reed-Xiaoli Detector (RXD) algorithm, and spectral indices in a three-variable feature space. Results indicate that floating aggregations are detectable on sub-pixel scales, but as reliable ground truth information was restricted to a single confirmed target, detections were only validated by means of their respective spectral responses. Effects of atmospheric correction algorithms were evaluated using ACOLITE, ACOMP, and FLAASH, in which derived unbiased percentage differences ranged from 1% to 81% following a pairwise comparison. Building first steps towards an integrated marine monitoring system, the strengths and limitations of current remote sensing technology are identified and adopted to make suggestions for future improvements

Monitoring the coastal zone using earth observation::application of linear spectral unmixing to coastal dune systems in Wales

Coastal sand dune systems across temperate Europe are presently characterized by a high level of ecological stabilization and a subsequent loss of biological diversity. The use of continuous monitoring within these systems is vital to the preservation of species richness, particularly with regard to the persistence of early stage pioneer species dependent on a strong sediment supply. Linear spectral unmixing was applied to archived Landsat data (1975?2014) and historical aerial photography (1941?1962) for monitoring bare sand (BS) cover dynamics as a proxy for ecological dune stabilization. Using this approach, a time series of change was calculated for Kenfig Burrows, a 6-km2 stabilized dune system in South Wales, during 1941?2014. The time series indicated that a rapid level of stabilization had occurred within the study area over a period of 75 years. Accuracy assessment of the data indicated the suitability of medium-resolution imagery with an RMSE of <10% across all images and a difference of <3% between observed and predicted BS area. Temporal resolution was found to be a significant factor in the representation of BS cover with fluctuations occurring on a sub-decadal scale, outside of the margin of error introduced through the use of medium-resolution Landsat imagery. This study demonstrates a tractable approach for mapping and monitoring ecologically sensitive regions at a subLandsat pixel levelpublishersversionPeer reviewe

Mapping and Risk Assessment of Juniper Encroachment Into a Prairie Landscape

Juniper encroachment is a considerable threat to the prairie ecosystems of the Great Plains because it has the potential to alter native grasslands by changing soil characteristics, limiting herbaceous biomass, and hindering native community regeneration. Accurate maps of juniper cover and predictions of areas at risk for future expansion are needed to support proactive management measures. Therefore, our objectives are to: (1) Develop a practical workflow for large-scale juniper mapping using Landsat 8 Operational Land Imager (OLI) imagery and partial unmixing techniques, (2) Compare the classification accuracies from the resulting map based on different juniper density thresholds and different types of imagery, (3) Develop a predictive spatial model for the distribution of low-density juniper based on distance to seed source and environmental covariates and determine the prediction accuracy, and (4) Use the resulting maps to evaluate the extent of current juniper establishment and the risk of future encroachment. The study area encompasses counties bordering the Missouri River in southeastern South Dakota and northeastern Nebraska and covering approximately 23,000 km2. We applied a matched filtering technique to classify juniper with snowcovered and snow-free winter imagery (December-March) and snow-free spring imagery (April-June). We found that using the snow-covered winter images suppressed background spectral signatures and resulted in a higher overall classification accuracy of 93.7% for juniper densities above 15 percent, compared to snow-free winter imagery and spring imagery. When characterizing juniper densities below 10 percent our 30-meter pixel level classification map was unreliable, with an 11% probability of correctly classifying juniper. Therefore, we used Random Forests, a machine-learning algorithm, to develop a model of low-density (≤ 15%) juniper based on classified juniper cover and other ecological factors. We used the receiver operating characteristics (ROC) curve to evaluate model predictions; accuracy was high with an area under the curve (AUC) of 0.884. Our susceptibility map indicated that an additional 7.7% of the study area currently contained low densities of juniper and had high to very high risk of future encroachment. This study will provide agencies and land managers with information and techniques needed to address juniper encroachment in the Northern Great Plains