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Tuia D.,University of Valencia | Volpi M.,University of Lausanne | Copa L.,University of Lausanne | Copa L.,Sarmap SA | And 2 more authors.
IEEE Journal on Selected Topics in Signal Processing | Year: 2011

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user. © 2011 IEEE. Source


Ismail R.,Sappi Forests | Ismail R.,University of KwaZulu - Natal | Kassier H.,Sappi Forests | Chauke M.,Sappi Forests | And 2 more authors.
Southern Forests | Year: 2015

In commercial forestry, regular terrestrial enumerations of the growing stock are required for the valuation, sustain-able management and planning of current and future timber supplies. In this study we examined whether the combination of synthetic aperture radar (ALOS PALSAR) and optical satellite (SPOT 4) image data can accurately predict the timber volume of even-aged Eucalyptus plantations located in South Africa. Results from this study show that the combination of ALOS PALSAR and SPOT 4 produces a R 2 value of 0.68 for the planted model, whereas the coppiced model produced a R 2 value of 0.55. However, by including stand age as an independent variable in the stepwise model, there was a 15% improvement for the planted model, whereas the coppiced model produced a 27% improvement. The final model developed in this study produced a R 2 value of 0.83 and a RMSE of 31.71 m3 ha−1 for planted stands, whereas the model for coppiced stands produced a R 2 value of 0.82 and a RMSE of 27.70 m3 ha−1. As it is not practical or financially feasible for commercial forestry companies to carry out terrestrial enumerations for all plantations on an annual basis, the model developed in this study presents an alternative and accurate method to calculate timber volume for even-aged Eucalyptus plantations. © 2015 NISC (Pty) Ltd. Source


Atwood D.K.,University of Alaska Fairbanks | Andersen H.-E.,University of Washington | Matthiss B.,Karlsruhe Institute of Technology | Holecz F.,Sarmap SA
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Synthetic aperture radar (SAR) has been shown to be a useful tool for estimating aboveground biomass (AGB), due to the strong correlation between the biomass and backscatter. In particular, L-band SAR is effective for estimating the lower range of biomass that characterizes most boreal forests. Unfortunately, the topographic impact on backscatter can dominate the normal forest signal variation. Since many boreal environments have significant topography, we investigate several topographic correction techniques to determine their effect upon AGB prediction accuracy. Different approaches to addressing the topography include: 1) no correction, 2) local incidence angle (LIA) correction, 3) pixel-area correction, and 4) a novel empirical slope correction. The investigation was performed for a data-rich experimental area near Tok, Alaska, for which Advanced Land Observing Satellite Phased Array type L-Band Synthetic Aperture Radar (ALOS PALSAR), field plots, lidar acquisitions, and a high-quality digital elevation model (DEM) existed. Biomass estimations were performed using both single- and dual-polarization (HH and HV) regressions against field plot data. The biomass estimation for each of the topographic corrections was compared with the field plot biomass, as well as more extensive lidar biomass estimations. The results showed a clear improvement in AGB estimation accuracy from no correction, to LIA, to pixel-area, to the novel pixel-area plus empirical slope correction. Using the field plot data for validation, the SAR root mean square error (RMSE) derived from the best approach was found to be 37.3 Mg/ha over a biomass range of 0-250 Mg/ha, only marginally less accurate than the 33.5 Mg/ha accuracy of the much more expensive lidar technique. © 2013 IEEE. Source


Baraldi A.,European Commission - Joint Research Center Ispra | Durieux L.,European Commission - Joint Research Center Ispra | Durieux L.,Institute Of Recherche Pour Le Developpement | Simonetti D.,European Commission - Joint Research Center Ispra | And 3 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

To date, the automatic or semiautomatic transformation of huge amounts of multisource multiresolution spaceborne imagery into information still remains far below reasonable expectations. The original contribution of this paper to existing knowledge on the development of operational automatic remote sensing image understanding systems (RS-IUSs) is fourfold. First, existing RS-IUS architectures are critically revised. In this review section, the two-stage stratified hierarchical RS-IUS model, originally proposed by Shackelford and Davis, is identified as a subclass of the parent class of multiagent hybrid systems for RS image understanding, which is potentially superior to the two-stage segment-based RS-IUS architecture that is currently considered the state-of-the-art in commercial RS image-processing software toolboxes. Second, this paper highlights the degree of novelty of an operational automatic near-real-time well-posed model-driven application-independent per-pixel Landsat-like spectral-rulebased decision-tree classifier (LSRC) recently presented in RS literature. Third, five original downscaled implementations of the LSRC system are proposed to be input with a multispectral image whose spectral resolution overlaps with, but is inferior to, Landsat's. These five downscaled LSRC implementations are identified as the Satellite Pour l'Observation de la Terre-like SRC, the Advanced Very High Resolution Radiometer-like SRC, the Advanced Along-Track Scanning Radiometer-like SRC, the IKONOS-like SRC, and the Disaster Monitoring Constellationlike SRC, respectively. LSRC, together with its five downscaled implementations, called the integrated SRC system of systems, is eligible for use as the automatic pixel-based preliminary classification first stage of a two-stage stratified hierarchical RS-IUS instantiation. Fourth, to sustain the feasibility of the new downscaled LSRC implementations, a novel vegetation spectral index is introduced and discussed. In Part II of this paper, experimental results are presented and discussed for the entire SRC family of classifiers. © 2009 IEEE. Source


Baraldi A.,European Commission - Joint Research Center Ispra | Durieux L.,European Commission - Joint Research Center Ispra | Durieux L.,CIRAD - Agricultural Research for Development | Simonetti D.,Center for Monitoring Research | And 3 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

In Part I of this paper, an operational fully automated Landsat-like image spectral rule-based decision-tree classifier (LSRC), suitable for mapping radiometrically calibrated sevenband Landsat-4/-5 Thematic Mapper (TM) and Landsat-7 Enhanced TM+ (ETM+) spaceborne images [eventually synthesized from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and theModerate Resolution Imaging Spectroradiometer (MODIS) imaging sensor] into a discrete and finite set of spectral categories, has been downscaled to properly deal with spaceborne multispectral imaging sensors whose spectral resolution overlaps with, but is inferior to Landsat's, namely: 1) Satellite Pour l'Observation de la Terre (SPOT)-4/-5, Indian Remote Sensing Satellite (IRS)-1C/-1D/-P6 Linear Imaging Self-Scanner (LISS)-III, and IRS-P6 Advanced Wide Field Sensor (AWiFS); 2) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Meteosat Second Generation (MSG); 3) Environmental Satellite (ENVISAT) Advanced Along-Track Scanning Radiometer (AATSR); 4) GeoEye-1, IKONOS-2, QuickBird-2, OrbView-3, TopSat, KOrean MultiPurpose SATellite (KOMPSAT)-2, FORMOsa SATellite (FORMOSAT)-2, Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), RapidEye, WorldView-2, PLEIADES-1/-2, and SPOT-6/-7; and 5) Disaster Monitoring Constellation (DMC), IRS-P6 LISS-IV, and SPOT-1/-2. LSRC, together with its five downscaled versions, identified, respectively, as the four-band SPOT-like SRC (SSRC), the four-band AVHRR-like SRC (AVSRC), the five-band AATSR-like SRC (AASRC), the four-band IKONOS-like SRC (ISRC), and the three-band DMClike SRC (DSRC), form the so-called integrated SRC system of systems. In this paper, first, the classification accuracy and robustness to changes in the input data set of SSRC, AVSRC, AASRC, ISRC, and DSRC are assessed, both qualitatively and quantitatively, in comparison with LSRC's. Next, ongoing and future SRC applications are presented and discussed. They encompass: 1) the implementation of operational two-stage stratified hierarchical Remote Sensing (RS) image understanding systems discussed in Part I of this paper; 2) the integration of near real-time satellite mapping services with Internet map servers; and 3) the development of a new approach to semantic querying of large-scale multisensor image databases. These experimental results and application examples prove that the integrated SRC system of systems is operational, namely, it is effective, near real-time, automatic, and robust to changes in the input data set. Therefore, SRC appears eligible for use in operational satellite-based measurement systems such as those envisaged by the ongoing international Global Earth Observation System of Systems (GEOSS) Programme and the Global Monitoring for Environment and Security (GMES) system project. © 2009 IEEE. Source

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