Image Time Series Analysis Research Articles

The Design of Application Model using Manufacturing Data in Protection Film Process for Smart Manufacturing Innovation

세계 제조업은 장기적인 경기침체, 노동 원가 및 원자재 가격 상승으로 성장 한계에 봉착하게 되었으며, 이에 대한 해결방안으로 ICT와 센서 기술을 바탕으로 제조업의 4차 산업혁명을 진행하고 있다. 이러한 흐름에 따라 화학 산업에서의 스마트공장 보급 확산과 스마트제조 기술 향상을 위해, 본 논문은 스마트 제조혁신을 위한 보호필름 공정 제조데이터 활용모델의 설계를 제안한다. 보호필름 공정 중에서 원료 배합 및 교반, 압출, 그리고 검수 공정에 대해서 온도, 압력, 습도, 그리고 동영상 및 열화상의 제조 데이터를 획득한다. 또한 획득된 제조 데이터는 대용량 스토리지에 저장되며, AI 서비스에 의한 시계열 및 이미지 분석과 시각화가 진행된다.The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.

Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning

Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We here assessed the use of annual, quarterly, and eight-day temporal features for mapping five cropping practices on annual croplands across Turkey. We used 2403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 to compute quarterly best-pixel composites, quarterly and annual spectral-temporal metrics, as well as gap-filled eight-day time series of Tasseled Cap components. We tested 22 feature sets for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. We evaluated area-adjusted accuracies and compared cropland area estimates at the province-level with official statistics. We achieved overall accuracies above 90%, when using either all quarterly features or the eight-day Tasseled Cap time series, indicating that temporal binning of intra-annual image time-series into multiple temporal features improves representations of cropping practices. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Our mapped cropland extent was in good agreement with province-level statistics (r2 = 0.85, RMSE = 7.2%). Our results indicate that 71.3% (±2.3%) of Turkey’s annual croplands were cultivated during winter and spring, 15.8% (±2.2%) during summer, while 8.5% (±1.6%) were double-cropped, 4% (±1.9%) were cultivated under semi-aquatic conditions, and 0.32% (±0.2%) was greenhouse cultivation. Our study presents an open and readily available framework for detailed cropland mapping over large areas, which bears the potential to inform assessments of land use intensity, as well as land and water resource demands.

DtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R

The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.

SAR image time-series analysis framework using morphological operators and global and local information-based linear discriminant analysis

Fusion of spectral, spatial, and temporal information is an effective method used in many satellite remote sensing applications. On the other hand, one drawback of this fusion is an increase in complexity. In this paper, we focus on developing a fast and well-performed classification method for agricultural crops using time-series SAR data. In order to achieve this, a novel two-stage approach is proposed. In the first stage, a high-dimensional feature space is obtained using time-series dual-pol SAR data and morphological operators. Spectral, spatial, and temporal information is combined into a single high-dimensional feature space. In the second stage, a dimension reduction technique is applied to the feature vector in order to decrease time complexity and increase classification accuracy by considering the global and local pattern information in the high-dimensional feature space. The contribution of the morphological profiles to the classification performance is significant; however the time complexity is increased drastically. The proposed method overcomes the time complexity stemming from high-dimensional feature space; it also improves the classification performance. The superiority of the proposed method to the comparative methods in agricultural crop classification is experimentally shown with the improvements in both classification and time performance using time-series TerraSAR-X images.

Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series

Rubber (Hevea brasiliensis) plantations are a rapidly increasing source of land cover change in mainland Southeast Asia. Stand age of rubber plantations obtained at fine scales provides essential baseline data, informing the pace of industrial and smallholder agricultural activities in response to the changing global rubber markets, and local political and socioeconomic dynamics. In this study, we developed an integrated pixel- and object-based tree growth model using Landsat annual time series to estimate the age of rubber plantations in a 21,115 km2 tri-border region along the junction of China, Myanmar and Laos. We produced a rubber stand age map at 30 m resolution, with an accuracy of 87.00% for identifying rubber plantations and an average error of 1.53 years in age estimation. The integration of pixel- and object-based image analysis showed superior performance in building NDVI yearly time series that reduced spectral noises from background soil and vegetation in open-canopy, young rubber stands. The model parameters remained relatively stable during model sensitivity analysis, resulting in accurate age estimation robust to outliers. Compared to the typically weak statistical relationship between single-date spectral signatures and rubber tree age, Landsat image time series analysis coupled with tree growth modeling presents a viable alternative for fine-scale age estimation of rubber plantations.

TOWARD AUTOMATIC GEOREFERENCING OF ARCHIVAL AERIAL PHOTOGRAMMETRIC SURVEYS

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

A multiscale subpixel mixture analysis applied for melt detection using passive microwave and radar scatterometer image time series of the Antarctic Peninsula (1999–2009)

ABSTRACTThis paper reports a comparative analysis performed on a fraction-image time series of the Antarctic Peninsula from the period 1999–2009 generated by multiresolution remote-sensing images (SSM/I and SSMI/S with 25 km and QuikSCAT with 2.225 km spatial resolutions) for snow-melt detection. Our method is based on the (a) preprocessing of multitemporal remote-sensing data, (b) subpixel mixture analysis of SSMI and QuikSCAT image time series, and (c) evaluation of subpixel analysis, including an assessment of fraction images of wet snow using an independent ASAR dataset and sensitivity analysis on the melt metrics measured by these images. The temporal dynamics of the melt indices derived from the wet-snow fraction images presented a more realistic pattern than the traditional melt metrics measured by Boolean snow-melt detection approaches. Because the snow melt actually occurs at the pixel fractions, the multiscale analysis that was performed suggests an overestimation of the melt metrics calculated using Boolean approaches (which assume that the entire area of the detected pixel shows snow melt). The melt metrics measurements show an overestimation according to the decrease in spatial resolution related to the multiplicative effect of a larger pixel area.

Object-oriented satellite image time series analysis using a graph-based representation

Nowadays, remote sensing technologies produce huge amounts of satellite images that can be helpful to monitor geographical areas over time. A satellite image time series (SITS) usually contains spatio-temporal phenomena that are complex and difficult to understand. Conceiving new data mining tools for SITS analysis is challenging since we need to simultaneously manage the spatial and the temporal dimensions at the same time. In this work, we propose a new clustering framework specifically designed for SITS data. Our method firstly detects spatio-temporal entities, then it characterizes their evolutions by mean of a graph-based representation, and finally it produces clusters of spatio-temporal entities sharing similar temporal behaviors. Unlike previous approaches, which mainly work at pixel-level, our framework exploits a purely object-based representation to perform the clustering task. Object-based analysis involves a segmentation step where segments (objects) are extracted from an image and constitute the element of analysis. We experimentally validate our method on two real world SITS datasets by comparing it with standard techniques employed in remote sensing analysis. We also use a qualitative analysis to highlight the interpretability of the results obtained.

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.

Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series

This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal analysis of image time series, which make use of the highly parallel and low-power-consuming neuromorphic hardware platforms possible. This paper illustrates this concept with the introduction of the first SNN computational model for crop yield estimation from normalized difference vegetation index image time series. It presents the development and testing of a methodological framework which utilizes the spatial accumulation of time series of Moderate Resolution Imaging Spectroradiometer 250-m resolution data and historical crop yield data to train an SNN to make timely prediction of crop yield. The research work also includes an analysis on the optimum number of features needed to optimize the results from our experimental data set. The proposed approach was applied to estimate the winter wheat ( Triticum aestivum L. ) yield in Shandong province, one of the main winter-wheat-growing regions of China. Our method was able to predict the yield around six weeks before harvest with a very high accuracy. Our methodology provided an average accuracy of 95.64%, with an average error of prediction of 0.236 t/ha and correlation coefficient of 0.801 based on a nine-feature model.

Spatio–Temporal Clustering and Active Learning for Change Classification in Satellite Image Time Series

Active learning (AL) has emerged as a versatile approach to reduce the training data required for remote sensing image classification, but its use for the analysis of satellite image time series (SITS) has not been explored yet. This study targets to explore a new object-based framework for change detection in SITS, combining the state-of-the-art spatio-temporal clustering and the use of different machine learning algorithms. Indeed, this study aims at testing whether standard machine learning algorithms can detect changes in long time series and whether AL can improve the results compared to traditional supervised learning. The tested AL algorithms comprise random forest-based heuristics that use a combination of uncertainty and diversity criteria, and a classical SVM breaking ties heuristic. The different implementations are evaluated with two datasets that depict changes around the Arcachon basin (West of France) and around the city of Colmar (East of France) spanning over more than 20 years and comprising four change classes (urban sprawl, forest gain, forest loss, and other). The tests demonstrate that steeper learning curves can be obtained with AL when compared to supervised learning. However, the performance of different AL algorithms depends on the dataset.

Wavelet Operators and Multiplicative Observation Models—Application to SAR Image Time-Series Analysis

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either: 1) a direct wavelet transform of the model; or 2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework 1, wavelet coefficients of the time series are affected by intricate correlation structures which blur signal singularities. Framework 2 is shown to be associated with a multiplicative (or geometric) wavelet transform, and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. This paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, this paper proposes a fast-and-concise geometric-wavelet-based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change images from the time series. The changes are then enhanced, and speckle is attenuated by using spatial block sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change images. Some applications highlight relevancy of the method for the analysis of SENTINEL-1A and TerraSAR-X image time series over Chamonix Mont Blanc.

Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields.

We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations.

SU‐D‐BRA‐05: Time Series Analysis of EPID Images to Identify Patients in Need of Treatment Adaptation

Purpose:to evaluate if time series analysis of portal dose images can be used to identify patients undergoing important anatomical changes.Methods:daily EPID images of every treatment fields were acquired for 48 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis (weekly or biweekly). Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g. average gamma value, standard deviation, percent above 1). The gamma parameters formed a patient‐specific time series that was analyzed. The first 24 patients were retrospectively evaluated to establish an action threshold that was then applied on the remaining 24 patients. Dosimetric evaluation of patients above that threshold was performed to as assess the level of degradation compared to the initial treatment plan.Results:after performing a clinical retrospective analysis of the first 24 cases, an action threshold on the average gamma value was established at 0.6 and a warning level was set at 0.4. These thresholds were then applied to the remaining 24 cases. Of these, 6 patients (25%) were above the warning level and 4 (17%) were above the action threshold. Dosimetric evaluation was performed on the CBCT for all these 6 patients. Three of these patients had changes above 3% in their PTV coverage, one had changes of about 2% and the remaining two had negligible changes. Patients with the strongest changes all had clear trending in their gamma parameters that could be classified with techniques such as hidden Markov models.Conclusion:by using time series analysis of relative EPID image it was possible to identify a subset of patient most likely to benefit from treatment adaptation.This work was funded in part by Varian Medical Systems

Vegetation productivity trends in response to urban dynamics

Urbanization is a global phenomenon with still unknown consequences for vegetation dynamics of urban ecosystems, especially in subtropical areas of developing countries. In this paper we analyze the vegetation productivity trend associated to urban densification and urban expansion during the last decade, in twelve cities of northern Argentina. We used time series analysis of MODIS-NDVI images to reconstruct the phenological patterns to retrieve a productivity trend under three spatial classes of urban dynamics: (1) urban, (2) expansion and (3) periphery. Our results show that trends in vegetation productivity are more associated to the environmental characteristics (basal productivity and climate) than to the land cover class. The average trend in productivity in urban areas ranged between −2.54% year−1 (Metán) and −0.22% year−1 (Concepción). In contrast, the range was much tighter between classes; it was −1.37% year−1 in urban areas and −1.21% in the periphery. In this sense we found significant differences between cities, but no significant differences were observed between classes. Urban growth and urban expansion patterns found in our study suggest the system dynamics is dominated by sprawl patterns rather than by a homogeneous densification. Related to this phenomenon, our results dismissed the idea of urban expansion as the main factor affecting vegetation phenology and supported the hypothesis of regional warming as an explanation for the decrease in vegetation productivity, probably due to the decrease of water balance in arid regions.

Determining the Type and Starting Time of Land Cover and Land Use Change in Southern Ghana Based on Discrete Analysis of Dense Landsat Image Time Series

Rural to urban migration and relatively high fertility rates have influenced rapid land cover and land use change (LCLUC) in southern Ghana, which warrants more frequent monitoring. We develop and test approaches for semiautomatically and more frequently identifying the type and date of LCLUC from time series of Landsat ETM+ imagery from 2000 to 2014. Clouds, cloud shadows, and scan line corrector-off create missing data in $\text{ETM}+$ images. Forty-one dates of $\text{ETM}+$ images that partially contain missing data were utilized. The general approach is to conduct a per-pixel supervised classification on each image of a Landsat time series after masking missing data. Spatial, temporal, and logical filters are applied to correct for misclassification and missing data. Each image is classified into three general classes: 1) Built; 2) Natural Vegetation; 3) and Agriculture, with expansion of Built being our main focus. Reference data for Change-to-Built were independently selected from all available high-spatial resolution satellite images (e.g., Quickbird, GeoEye, Worldview, and Google Earth imagery), and the type and beginning time of LCLUC was recorded. Results show that the temporal-filtered product identified both the location and the start of Change-to-Built more precisely and accurately than the nonfiltered and other filtered products. Based on reference data, 40% of the Change-to-Built samples were correctly identified without filtering; whereas, when a temporal filter was applied, 80% were correctly identified with low amounts of false positive Change-to-Built pixels. The temporal-filtered product has the highest temporal precision and accuracy ( $\text{mean time difference} = 2.1 \text{years}$ ) in identifying the start of Change-to-Built.

Using multi-remote sensing data to assess Phragmites invasion of the Detroit river international wildlife refuge

Purpose This paper aims to develope an integrated image processing method to investigate the spatiotemporal dynamics of Phragmites invasion in the Detroit River International Wildlife Refuge on the basis of publically available sources. Design/methodology/approach This new approach integrates the standard time-series analysis of Landsat images with USDA National Agriculture Imagery Program (NAIP) imagery and USGS Digital Orthophoto Quarter Quads (DOQQ) datasets, which are either classified or manually interpreted with the aid of ground control points. Three different types of spatiotemporal dimensions are designed to test this integrated time-series image analysis method: the selected sites and selected time-points with high spatial resolution and sufficient validation data points, the intermediate time-series with continued yearly images and periodical validation data, and the long time-series with periodical images without enough validation data. The support vector machine (SVM) method was used to classify the Landast TM sequence images to detect the Phragmites invasion. Findings The habitat map produced by NAIP images and field collection data shows that the total Phragmites area of DRIWR in 2010 is 4221.87 acres without treatment areas and similar with the removed non-vegetation method. It is confirmed that the pre-classification method can obtain more accurate results. Originality value The test results show that the Landsat-5 data can be used for long-term environmental management and monitoring of Phragmites invasion and can achieve rehabilitation of invasion areas.

Vegetation Regrowth Following Wildfires in the Santa Cruz Mountains of Northern California Monitored Using Landsat Satellite Image Analysis

The Santa Cruz Mountain range in northern California is a coastal landscape with a history of extensive forest logging and frequent large wildfires that have recently destroyed numerous residential structures at the wildland interface. Results from Landsat satellite image time-series analysis since 1984 of the study area within the Los Gatos Creek and Corralitos Creek watersheds showed that none of the severe drought periods since the 1980s have notably inhibited rapid tree and shrub regrowth rates on steep hill slopes burned recently by the 1985 Lexington Fire and the 2008 Summit Fire. In high burn severity areas of both fires, post-fire vegetation types showed a marked increase in shrub cover, mainly at the expense of evergreen tree cover. Most of these low (·ha-1 of standing woody biomass on the burned areas. This study is the first of its kind to utilize a full 30-year record of Landsat vegetation index data to monitor tree and shrub regrowth after stand-replacing wildfires in California.

Thirty years of vegetation change in the coastal Santa Cruz Mountains of Northern California detected using landsat satellite image analysis

The Santa Cruz Mountains is a coastal landscape with a history of extensive forest logging, and a future with projected climate warming that may alter vegetation cover and surface water runoff in new ways. Results from Landsat satellite image time-series analysis since 1983 of this study area showed gradual, statistically significant increases in the normalized difference vegetation index (NDVI) in more than 90 % of the (predominantly second-growth) evergreen forest locations sampled. The cumulative distribution of NDVI values in 2013 was significantly different and higher overall from the cumulative distribution of NDVI values in 1983. The extreme drought year of 2013 (and other previous years of low precipitation) did not affect average NDVI growth rates in most drainage basins of the study area, with the exception of four relatively small basins that had less than 30 % forested land cover. Notably different patterns of NDVI change were detected in areas burned by wildfires in recent years. Within the perimeters of the 2008 Summit Fire and the 2009 Lockheed Fire, NDVI showed notable declines from pre-fire levels to those calculated in 2013 Landsat imagery. In contrast to these recent fires, the burned area from the 1985 Lexington Fire showed the highest rate of NDVI increase (over 27 years of regrowth) of any relatively large contiguous area within the Santa Cruz Mountains.

Remote sensing time series analysis for crop monitoring with the SPIRITS software: new functionalities and use examples

Monitoring crop and natural vegetation conditions is highly relevant, particularly in the food insecure areas of the world. Data from remote sensing image time series at high temporal and medium to low spatial resolution can assist this monitoring as they provide key information about vegetation status in near real-time over large areas. The Software for the Processing and Interpretation of Remotely sensed Image Time Series (SPIRITS) is a stand-alone flexible analysis environment created to facilitate the processing and analysis of large image time series and ultimately for providing clear information about vegetation status in various graphical formats to crop production analysts and decision makers. In this paper we present the latest functional developments of SPIRITS and we illustrate recent applications. The main new developments include: HDF5 importer, Image re-projection, additional options for temporal Smoothing and Periodicity conversion, computation of a rainfall-based probability index (Standardized Precipitation Index) for drought detection and extension of the Graph composer functionalities. In particular,. The examples of operational analyses are taken from several recent agriculture and food security monitoring reports and bulletins. We conclude with considerations on future SPIRITS developments also in view of the data processing requirements imposed by the coming generation of remote sensing products at high spatial and temporal resolution, such as those provided by the Sentinel sensors of the European Copernicus programme.