Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.
Knowing the exact growth stage of agricultural crops can be valuable information for crop management and monitoring. In Canada, canola fields are particularly vulnerable for crop disease development during their flowering stage, especially when the fields are under persistent wet conditions. Clubroot and sclerotinia are diseases that can occur in canola when these two factors come together. Remote sensing can provide an interesting tool for the monitoring of crop phenological stages over large agriculture landscapes. Reliable and frequent access to data is needed to determine field-specific growth stages. Given their all-weather capability, radar sensors are optimal for monitoring such a dynamic crop parameter. In 2014, Agriculture and Agri-Food Canada collected crop phenology information over multiple canola fields in the area of Carman, Manitoba. Coincidental to ground data collection, fully polarimetric RADARSAT-2 and dual-polarimetric TerraSAR-X satellite data were acquired over the study site. In collaboration with A. U. G. Signals Ltd., a methodology will be developed and validated for the identification of inflorescence emergence and flowering in canola fields. Analysis of the polarimetric datasets from this study determined that several polarimetric parameters were sensitive to the emergence of flower buds and the flowering stage in canola. The alpha angle and entropy in both the C- and X-band were able to identify these growth stages, in addition to any of the reflectivity ratios and differential reflectivity responses that incorporated an HV response. The RADARSAT-2 scatter diversity, degree of purity and depolarization index also demonstrated great potential at identifying canola flower emergence and flowering. These latter polarimetric parameters along with the reflectivity ratios may be advantageous given their ease in implementation within a larger risk assessment satellite-derived methodology for canola crop disease.
In this paper, tradeoff studies on several pixel level fusion algorithms and on their performance evaluation criteria are presented. Electro-optical (EO) and SAR sensors are dissimilar and produce images with very low degrees of correlation. These images are initially registered at subpixel level accuracy. The fusion is performed using the following pixel level fusion algorithms: Principal Component Analysis (PCA), Averaging (Ave), Laplacian Pyramid, Filter Subtract Decimate (FSD), Ratio Pyramid, Contrast Pyramid, Gradient Pyramid, Discrete Wavelet Transform (QWT), Shift Invariant DWT (SIDWT) with Haar, Morphological Pyramid, and the recent image fusion method developed by AUG Signals Ltd. A MATLAB based dedicated image fusion toolbox, that includes several pixel level fusion, restoration and registration algorithms, has been recently developed by AUG Signals. This toolbox is used for the tradeoff studies.
In this paper we present a new Web-based application for registering multi-sensor satellite images for image fusion operations. It is a distributed processing system which offers automatic or semi-automatic image registration and it is intended to provide a service to the Canadian Geospatial Data Infrastructure (CGDI) users through the GeoConnections Discovery Portal, formerly CEONet. It will be also provided on the web page of A.U.G. Signals Ltd.(www.augsignals.com) which will be connected to CEONet and CGDI. This innovative technology of A.U.G. Signals has all the advantages of current registration techniques, plus is can estimate reference (control) points automatically at high degree of accuracy and with zero false alarms. Users who apply existing remote sensing software tools, such as PCI or IDL/ENVI, with geo-referenced points for registration, may employ the A.U.G. Signals software to further improve the registration accuracy of their images. Geo-referenced control points may also be used with the proposed software. The proposed service is expected to evolve and expand other distributed processing initiatives of current interest, such as the emerging GRID technologies under development in United States and Europe and the Canadian high-speed network CA*Net3 and be part of the US OGC Web based Initiative.
This paper's objective is to present a new, computationally efficient method for automatic exploration, detection and recognition. The automatic mineral homogeneous region separation algorithm developed by A.U.G. Signals in cooperation with the Canadian Space Agency (CSA) using AVIRIS data and mineral signatures from the Nevada's (U.S.) Cuprite site is described. The hyperspectral data and spectral signatures were provided by the Canada Center for Remote Sensing (CCRS). The algorithm is able to successfully divide the image in regions where the mineral composition remains constant. Hence, it can be used for reducing the noise is estimating the abundance parameters of the minerals on a pixel-by-pixel basis, for image region selection and hyperspectral image labeling for data storage and/or selective transmission. This may be another form of lossless hyperspectral image compression. Through the presented approach we are able to: a) divide a hyperspectral image into regions of adaptivity where pixel unmixing algorithms are able to extract the abundance parameters with higher degree of confidence, b) increase the signal to noise ration (SNR) of the present spectral signatures in a region and c) apply the proposed hyperspectral homogeneous region separation for data reduction (hyperspectral image compression). Experimental and theoretical results and comparisons/tradeoff studies are presented.
In this paper, we present the formulation of the problem for recognition of targets from hyperspectra images. It is shown that conventional recognition techniques may be extended to hyperspectra images for the distribution process. It is also shown that the recognition process is directly proportional to the number of multispectra frames that represent each target. For the discrimination process we propose a parametric and a nonparametric process in which both are extensions to Fisher and Fukunaga-Mantock methods respectively. Examples that show the composition of hyperspectra features are presented.
Tracking multiple maneuvering targets in clutter is a challenging problem. Using only the measured kinematic quantities is usually not adequate to meet the requirements on a multiple target tracking (MTT) system, i.e., to partition the sensor data into tracks of the targets while suppressing the clutter and false alarms. The efficient use of attribute data in addition to the kinematic measurements can greatly enhance the capability of an MTT system in discrimination against the false tracks. In this paper, the friend-foe identification information, the target run-length which measures the number of hits by a radar for a target, and the estimated target speed at each update of a track are used for true and false track identification. Since not all the information are available for all the tracks at all time, Dempster-Shafer's evidential reasoning is employed to combine these pieces of uncertain information with different levels of abstraction. Real air surveillance radar data were collected to evaluate the effectiveness of this combined tracking and identification approach. Results shows that the fusion of track attribute data with the kinematic estimates by Dempster-Shafer reasoning provides very satisfactory discrimination between the true and false tracks, thus greatly improves the system's surveillance capability over the system that uses only the kinematic data.
In this paper, two new centralized tracking systems are proposed. The first system employs a fusion center to preprocess multisensor measurements. In the fusion center, a novel multisensor track initiation processor based on the logic-based initiation concept is used to determine new tracks. The output of the fusion center are fused measurements which are sent to a multitarget tracker, such as nearest neighbor or joint probabilistic data association filter, to produce the global state estimates. The second centralized system is a fully centralized tracking architecture in the sense it does not use fusion as a preprocessor as in the first case. In this system, multisensor data association and multisensor track initiation are used with a standard Kalman filter to perform multisensor tracking. Both centralized tracking systems are evaluated in various tracking environments. Comparisons between the two systems indicate that the first system is more efficient in eliminating clutters. The second one requires fewer scans to initiate tracks and has better performance and a lower computational complexity.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.