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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Long- Term P-Band Tomosar Observations from the Borealscat Tower Experiment
摘要： SAR tomography at P-band allows the separation of scatterers throughout the vertical extent of a forest canopy, offering a SAR observable that can be used for biomass estimation. But the vertical backscattering distribution of forests are sensitive to changes in the weather and seasons, effects which are poorly understood. In this study, a tower-based radar is used to produce fully-polarimetric tomographic images of a boreal forest at P-band which are analysed over a period of one year. The largest variations seen were due to sub-zero temperatures, causing a drop in the effective scattering height. Seasonal changes in soil moisture and temperature caused a drop in ground-level backscatter at HH-polarisation and a drop in canopy-level cross-polarised backscatter during the summer.
关键词： boreal forest,long time series,ground-based radar,SAR tomography,polarimetry,BIOMASS
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spatially Precise Contextual Features Based on Superpixel Neighborhoods for Land Cover Mapping with High Resolution Satellite Image Time Series
摘要： High resolution image time series as those provided by Sentinel-2 allow to target semantically rich nomenclatures for land cover mapping. However, at 10m resolution, pixel based classification fails to correctly identify some classes for which pixel context is discriminative. Recent advances in deep convolutional neural networks show promising results to tackle this problem, but the lack of complete annotation over large areas, the computational cost and the dimensionality of the feature space (much larger than those used in computer vision) does not allow to use these approaches in operational mapping applications yet. Contextual information can be calculated by applying a fixed-size neighborhood filter, but this can cause the loss of linear objects and the rounding of sharp corners. In Object Based Image Analysis, segmentation is used to extract objects for calculating contextual features while maintaining the high-frequency elements in the image. However, these do not necessarily include spectrally diverse pixels in a neighborhood, which can be relevant for characterizing the context. Superpixels place themselves in between the fixed-neighborhood and the object-based methods, in that they include spectrally diverse pixels in the same segment by imposing size and compacity constraints, while remaining adaptive to the natural boundaries in the image. This study assesses and compares the ability of these three types of neighborhood to improve classification performance on context-dependent classes, in a high-resolution Sentinel-2 time series land cover mapping problem.
关键词： OBIA,Land Cover Mapping,Contextual features,Superpixel,Time-series
更新于2025-09-09 09:28:46
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A Spectral–Temporal Patch-Based Missing Area Reconstruction for Time-Series Images
摘要： Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the similar pixel is independently selected, the improper reference pixel or various accuracies obtained by different land covers causes the problem of salt-and-pepper noise in the reconstructed part of an image. To overcome these problems, this paper presents a spectral–temporal patch (STP)-based missing area reconstruction method for time-series images. First, the STP, the pixels of which have similar spectral and temporal evolution characteristics, is extracted using multi-temporal image segmentation. However, some STP have Missing Observations (STPMO) in the time series, which should be reconstructed. Next, for an STPMO, the most similar STP is selected as the reference STP; then, the mean and standard deviation of the STPMO is predicted using a linear regression method with the reference STP. Finally, the textural information, which is denoted by the spatial configuration of color or intensities of neighboring pixels, is extracted from the clear temporal-adjacent STP and “injected” into the missing area to obtain synthetic cloud-free images. We performed an STP-based missing area reconstruction experiment in Jiangzhou, Chongzuo, Guangxi with time-series images acquired by wide field view (WFV) onboard Chinese Gao Fen 1 on 12 different dates. The results indicate that the proposed method can effectively recover the missing information without salt-and-pepper noise in the reconstructed area; also, the reconstructed part of the image is consistent with the clear part without a false edge. The results confirm that the spectral information from the remaining clear part of the same image and textural information from the temporal-adjacent image can create seamless time-series images.
关键词： missing area reconstruction,cloud-free time-series image,cloud and cloud shadow,multi-temporal image segmentation
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Monitoring Rice Crops in Piemonte (Italy): Towards an Operational Service Based on Free Satellite Data
摘要： Rice plays an important role in Italy and particularly in Piemonte Region (NW Italy). It heavily impacts on waters resources determining critical situations related to irrigation management. This work, stimulated by the Agriculture Department of Piemonte Region Administration, tries to point out the potentialities of freely available satellite data to describe both agronomic and water dynamics of rice during its phenological season. SAR (Synthetic Aperture Radar) measurements from Sentinel-1 mission, proved to be effective in describing water dynamics and structure variations of crop. Temporal profiles of the SAR back-scattered signal (σ0) were used to describe submersion phases and structural changes of crops. Differently, optical data from Sentinel-2 and Landsat 8 missions, were jointly used to monitor crop health and water content after plants emersion. Spectral indices (NDVI, NDWI, GRVI) time series were used for this purpose. Results, for the 2016 year, demonstrate that this integrated approach can well describe the main rice crop agronomic phases.
关键词： Landsat,time series,agronomic services,Sentinel,rice
更新于2025-09-09 09:28:46
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Use of artificial neural networks and geographic objects for classifying remote sensing imagery
摘要： The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.
关键词： time series,object-based classification,image segmentation
更新于2025-09-09 09:28:46
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A Novel Automatic Method for Alfalfa Mapping Using Time Series of Landsat-8 OLI Data
摘要： Remote sensing (RS) data have been utilized increasingly for mapping various crops at local and regional scales using various techniques. However, training data collection of these methods is costly and time consuming. On the other hand, time series of RS data have provided valuable information about crop phenological patterns, which can be utilized for automatic crop mapping independent of training data. Hence, the aim of this research is to develop a new automatic method to map alfalfa by identification of specific characteristics of alfalfa based on time series of Landsat 8 OLI images in four study sites in Iran and the United States. Alfalfa fields are usually harvested periodically and two neighboring farms may not be harvested simultaneously. To address this challenge, the alfalfa spectral reflectance values in various bands were compared with those of other crops during the growing season. In the following, three assumptions were made to find suitable relationships for demonstrating alfalfa characteristics as well as separating it from other crops. The results indicated that the summation of differences between the red and NIR reflectance values of alfalfa in the time series of Landsat images is significant; and also, the average values of the NIR and red bands during the growing season are remarkably higher and lower than those of other crops, respectively. Hence, based on these findings, a new specific feature was developed to detect alfalfa with the overall accuracy of 93%, 90%, 94%, and 90% in Moghan, Qazvin, Razan, and Parker Valley, respectively.
关键词： phenology,Landsat time series,spectral feature,Alfalfa,automatic crop mapping
更新于2025-09-09 09:28:46
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Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring
摘要： The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.
关键词： permanent meadows,change detection,crop monitoring,arable fields,NDVI object-based temporal profiles,GEOBIA,time series analysis
更新于2025-09-09 09:28:46
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Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States
摘要： Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites—7 and 8—provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM+. Despite differences in their spatial, spectral, and temporal characteristics, integration of these datasets appears to be feasible through the application of bandwise linear regression corrections.
关键词： Sensor integration,ETM+,Sentinel-2,MSI,OLI,Time series,Change detection,Landsat
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Study of Landslide Characteristics Using Time-Series InSAR Technique
摘要： Xinmo landslide event, China occurred on 24 June 2017. The head of the landslide area was ~3,600m above the mean sea level. Then, along the high to low elevation direction, a stable zone (S1) above the head area, an unstable zone (that is divided into an upper unstable subsidence area, UU; and a lower unstable uplift area, LU), and a stable area (S2) before the landslide event were identified. The time-series surface deformation was studied using the SBAS-InSAR technique and Sentinel-1 multi-temporal datasets acquired between November of 2015 and June of 2017. The mean deformation value was –8.3mm in S1, –26.9mm in UU, 27.9mm in LU, and –1.6mm in S2, respectively. Zoning characteristics of moving earth materials in a typical landslide event were observed. After removing the deformation values in two stable zones, the difference in absolute value of the subsidence and uplift in the unstable zone was about 80mm, 100mm, and 130mm on 24 February 2017, 19 May 2017, and 24 June 2017, respectively. No landslide event occurred in February and May. Thus, with the preliminary findings, a warning might be issued once the absolute value was greater than 100mm.
关键词： Stable and unstable zones,Time-series surface deformation,Landslide,SBAS-InSAR technique
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Neural Networks Based Semantic Segmentation for Optical Time Series
摘要： Semantic segmentation or classification for satellite image time series (SITS) is a rarely touched topic, partly due to the difficulty in having the data, but more due to the unreachable task. In this research, we propose a dataset which consists of the Landsat image time series, with the purpose of performing multi-spectral semantic segmentation. As there is no ground truth information, we used unsupervised clustering to group time series into clusters, then Long short term memory (LSTM) unit based Recurrent neural networks (RNN) has been trained. We investigate the accuracy values for our test image patches, around 40% accuracy has been achieved for the sequence classification.
关键词： Temporal pattern,RNN,Satellite image time series,LSTM
更新于2025-09-09 09:28:46