Benoit Stoll

Support Vector Machine for Multifrequency SAR Polarimetric Data Classification

The objective of this paper is twofold: first, to assess the potential of radar data for tropical vegetation cartography and, second, to evaluate the contribution of different polarimetric indicators that can be derived from a fully polarimetric data set. Because of its ability to take numerous and heterogeneous parameters into account, such as the various polarimetric indicators under consideration, a support vector machine (SVM) algorithm is used in the classification step. The contribution of the different polarimetric indicators is estimated through a greedy forward and backward method. Results have been assessed with AIRSAR polarimetric data polarimetric data acquired over a dense tropical environment. The results are compared to those obtained with the standard Wishart approach, for single frequency and multifrequency bands. It is shown that, when radar data do not satisfy the Wishart distribution, the SVM algorithm performs much better than the Wishart approach, when applied to an optimized set of polarimetric indicators.

Support vector machines to map rare and endangered native plants in Pacific islands forests

article i nfo It is critical to know accurately the ecological and geographic range of rare and endangered species for biodiver- sityconservationandmanagement.Inthisstudy,weusedsupportvectormachines(SVM)formodelingrarespe- ciesdistributionand we compared ittoanother emergingmachinelearning classifiercalledrandom forests (RF). The comparison was performed using three native and endemic plants found at low- to mid-elevation in the is- land of Moorea (French Polynesia, South Pacific) and considered rare because of scarce occurrence records: Lepinia taitensis (28 observed occurrences), Pouteria tahitensis (20 occurrences) and Santalum insulare var. raiateense (81 occurrences). We selected a set of biophysical variables to describe plant habitats in tropical high volcanic islands, including topographic descriptors and an overstory vegetation map. The former were extracted from a digital elevation model (DEM) and the latter is a result of a SVM classification of spectral and textural bands from very high resolution Quickbird satellite imagery. Our results show that SVM slight- ly but constantly outperforms RF in predicting the distribution of rare species based on the kappa coefficient and the area under the curve (AUC) achieved by both classifiers. The predicted potential habitats of the threerarespecies are considerably wider than their currently observed distribution ranges. We hypothesize that the causes of this discrepancy are strong anthropogenic disturbances that have impacted low- to mid- elevation forests in the past and present. There is an urgent need to set up conservation strategies for the endangered plants found in these shrinking habitats on the Pacifi ci slands.

Classification of Tropical Vegetation Using Multifrequency Partial SAR Polarimetry

This letter presents a case study addressing the comparison between different synthetic aperture radar (SAR) partial polarimetric options for tropical-vegetation cartography. These options include compact polarization (CP), dual polarization (DP), and alternating polarization (AP). They are all derived from fully polarimetric (FP) SAR data acquired by the airborne SAR (AIRSAR) sensor over the French Polynesian Tubuai Island. The classification approach is based on the support vector machine algorithm and is further validated by several ground surveys. For a single frequency band, FP data give significantly better results than any other partial polarimetric configuration. Among the partial polarimetric architectures, the CP mode performs best. In addition, the DP mode shows better performance than the AP mode, highlighting the value of the polarimetric differential phase. The combination of different frequency bands (P-, L-, and C-bands) holds the most significant improvement: The multifrequency diversity adds generally more information than the multipolarization diversity. A noticeable result is the major contribution of the C-band at VV polarization (the only polarization available at C-band with the AIRSAR data set used in this letter) to the classification performance, due to its ability to discriminate between Pinus and Falcata.

SVM Selective Fusion (SELF) for Multi-Source Classification of Structurally Complex Tropical Rainforest

Accuracy of land cover classification is generally improved by inputting multi-sensory and GIS data since complex vegetation type identification benefits from synergism of complementary information. However, multi-source fusion can also deteriorate accuracy when some classes do not benefit from all sources. On the basis of this premise, we introduce a Selective Fusion (SELF) scheme based on Support Vector Machines (SVM) which use a single source for source-specific classes and fuse all sources for classes considered as “in difficulty”. Our method yields better overall accuracy and Kappa than the classical systematic approach since it takes advantage of the accuracy achieved by SVM and its ability to weight numerous and heterogeneous sources without the drawback of being sensible to irrelevant data for source-specific classes. This operational method can be used efficiently to enhance accuracy when analyzing the wealth of information available from remote sensing products.

A generalized ICA algorithm

In this paper, we consider the source separation problem through a block algorithm based on the maximization of contrast functions. We propose a new contrast with parameterized cross-cumulants. It allows us to put three classical contrasts in a common framework. Following the same spirit of the independent component analysis (ICA) algorithm, we derive the analytical solution for the case of two sources. Finally, a computer simulation is performed to illustrate the behavior of a Jacobi-like algorithm for the maximization of the new contrast.

Simulating social-ecological systems: the Island Digital Ecosystem Avatars (IDEA) consortium

Systems biology promises to revolutionize medicine, yet human wellbeing is also inherently linked to healthy societies and environments (sustainability). The IDEA Consortium is a systems ecology open science initiative to conduct the basic scientific research needed to build use-oriented simulations (avatars) of entire social-ecological systems. Islands are the most scientifically tractable places for these studies and we begin with one of the best known: Moorea, French Polynesia. The Moorea IDEA will be a sustainability simulator modeling links and feedbacks between climate, environment, biodiversity, and human activities across a coupled marine–terrestrial landscape. As a model system, the resulting knowledge and tools will improve our ability to predict human and natural change on Moorea and elsewhere at scales relevant to management/conservation actions.

Ground truth method assessment for SVM-based landscape classification

Researches on land cover classification have a complete lack of ground truth methodology description. We propose a method to track ecotones as privileged training areas for SVM-based natural vegetation classification. This guidance method combines (i) the construction of a principal component analysis (PCA) on spectral bands and gray level co-occurence matrix texture attributes calculated on very high resolution images and (ii) the use of the Sobels edge detection algorithm on this PCA. The experiment is successfully applied with an overall accuracy of 82 %. Using SVM, a minimum number of mixed pixels is necessary but they can help significantly in locating an appropriate hyperplane. Moreover, the presented results show that the training stage could be more influential on classifier accuracy than classifiers themselves.

“Fusion for classes in difficulty” for accurate and speed tropical rainforests classification

The accuracy of rainforests classification is generally improved by the input of multisensory data since complex vegetation type identification benefits from complementary information. However, in some cases, multisource fusion can also deteriorate accuracy when irrelevant sources are added. Thus, we introduce a fusion method for classes “in difficulty”. Our method outperforms the classical global approach consisting in performing fusion for all classes. Moreover, the fusion processing time can significantly decrease when several classes are put aside. This operational method can be used effectively to enhance accuracy and processing speed when analyzing the wealth of information available from remote sensing products.

Toward a Global Tuamotu Archipelago Coconut Trees Sensing Using High Resolution Optical Data

This study is part of a regeneration program of the coconut grove of French Polynesia where most coconut palm trees of the Tuamotu archipelago were planted in the 1980s following the various hurricanes that had struck islands. The French Polynesia government acquired one-meter pansharpened RGB Ikonos images over the Tuamotu archipelago. To exploit these data, a pilot study is conducted on the island of Tikehau, well-known from the specialists and easily accessible from Tahiti. A maximum likelihood (ML) classification is performed to segment the high vegetation in images. Thus, a support vector machines (SVM) classification allows the high vegetation to be classified in different patterns. And finally, a robust segmentation process based on markers controlled watershed segmentation is proposed to extract tree crowns. Through the ground mission, the trees detection accuracy is estimated which is then used to compute the number of trees the closest to the reality by applying a weighted factor to the number of trees located in each class.

SVM texture classification for tropical vegetation mapping

Nowadays, remote sensing is an essential science in French Polynesia because of its extended territory and the remoteness of its 120 islands. There is a strong need to study the vegetation cover and its evolution (biodiversity threat, invasive species, etc.). A growing satellite images database has been acquired throughout, giving access to very high resolution optical images such as Quickbird data. These data allow accessing the vegetation canopy spectral and contextual information, texture classification has proved to be an efficient tool to map the complex vegetation found in tropical regions. The main goal of this paper is to propose an optimized SVM multispectral-texture classification method for tropical vegetation mapping. One of the texture computation drawbacks is the window treatment size, which is related to the largest texture element size. In complex tropical vegetation cover, this parameter leads to very small ground truth learning database, inducing a significant degradation of the classifications accuracy. We propose to increase the thumbnail numbers using an under-sampling method, optimizing the size and the number of the thumbnails. The other drawback is the high dimensionality of the problem when dealing with multispectral textures. We thus propose to rank and select the most pertinent textures attributes in order to reduce the dimensionality without reducing the classification accuracy. We first introduce the study context, before exposing preliminary studies on tuning the SVM learning method. The adapted method is then accurately exposed and the interesting experimental results as well as a sample of applications are presented before to conclude.