Remote sensing image classification

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Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review.

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review.

Fig. 13. Number of Studies that used SVM or RF algorithms in different remote sensing applications. B. Statistical and remote sensing software packages A comparison of the geospatial and image processing software and other statistical packages is depicted in Fig. 14. These software packages shown here were used for the implementation of both the SVM and RF methods in at least three journal papers. The software packages include eCognition (Trimble), ENVI (Harris Geospatial SolutionsInc.), ArcGIS (ESRI), Google Earth Engine, Geomatica (PCI Geomatics) as well as statistical and data analysis tools, which are Matlab (MathWorks), and open- source software tools such as R, Python, OpenCV, and Weka data mining software tool (developed by Machine Learning Group at the University of Waikato, New Zealand). A detailed search through the literature showed that the free statistical software tool R appears to be the most important and frequent source of SVM (25%) and RF (41%) implementation. R is a programming language and free software environment for statistical computing and graphics and widely used for data analysis and development of statistical software. Most of the implementations in R were carried out using the caret package [149], which provides a standard syntax to execute a variety of machine learning methods, and e1071 package [150], which is the first implementation of SVM method in R. The dominance of statistical and data analysis software especially R, Python (scikit-learn package), and Matlab is mainly because of the flexibility of these interfaces in dealing with extensive machine learning frameworks such as image preprocessing, feature selection and extraction, resampling methods, parameter tuning, training data balancing, and classification accuracy comparisons. In terms of the commercial software, for RF, eCognition is the most popular one, with about 17% of the case studies, while for the SVM classification method, ENVI is the most frequent software accounted for 9% of the studies.
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Recent Developments from Attribute Profiles for Remote Sensing Image Classification

Recent Developments from Attribute Profiles for Remote Sensing Image Classification

Index Terms—mathematical morphology, attribute profiles, multilevel image description, image classification, remote sensing I. I NTRODUCTION Image classification is one of the most crucial tasks in remote sensing imagery which serves for several applications in land use and land cover mapping and monitoring. With the emergence of high resolution remote sensing technology, the exploitation of the spatial information together with the spectral characteristics becomes more and more significant to characterize and discriminate different thematic classes present from the image content. Within such spatial-spectral context, morphological profiles (MPs) [1] were extensively exploited during the 2000’s [2]–[5] thanks to their multilevel analysis of spatial information by applying a sequence of opening and closing by reconstruction operators with increasing-size structuring elements (SEs). However, their high computation complexity prevent them to deal with large-size images. Be- sides, SEs can only model the size and scale of regions without their gray-level characteristics, thus not considering contextual features such as texture and contrast.
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End-to-End Learning of Polygons for Remote Sensing Image Classification

End-to-End Learning of Polygons for Remote Sensing Image Classification

ABSTRACT While geographic information systems typically use polygonal representations to map Earth’s objects, most state- of-the-art methods produce maps by performing pixelwise classification of remote sensing images, then vectorizing the outputs. This paper studies if one can learn to directly output a vectorial semantic labeling of the image. We here cast a mapping problem as a polygon prediction task, and propose a deep learning approach which predicts vertices of the poly- gons outlining objects of interest. Experimental results on the Solar photovoltaic array location dataset show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs.
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Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification

Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification

However, the direct exploitation of APs or SDAPs for classification task may be insufficient for a complete char- acterization of structural and textural information from the image, especially when regions and objects become more heterogeneous in images acquired by very high resolution (VHR) remote sensing sensors. That is why in [11], the authors proposed the histogram-based attribute profiles (HAPs) for another enhancement of APs. HAPs are built by concatenating the local histograms of attribute filter responses of each pixel. They have been proved to be more efficient and to better deal with local textures in VHR images [11]. However, two limitations of HAPs can be observed involving their very high dimensionality and their high sensitivity to the number of histogram bins (more details will be provided in the rest of the paper). Therefore in this paper, instead of constructing local histograms, our motivation is to exploit certain statistical features to characterize the local neighborhood around each pixel. Similar to HAPs, the proposed local feature-based attribute profiles (LFAPs) can provide a better description of local textures in VHR images than the standard APs. By using some simple first-order local features, LFAPs can overcome the two aforementioned drawbacks of HAPs. Furthermore, the construction of LFAPs is not limited to the use of first-order statistical features, one can take into consideration other kinds of local features to tackle more complex VHR image scenes. Analogously, we also propose to build the LFSDAPs by extracting and combining some first-order local features from SDAPs. Then, to deal with hyperspectral image data, the extended versions of LFAPs and LFSDAPs (namely ELFAPs and ELFSDAPs, respectively) will be also derived. They are constructed by stacking all the features obtained from some first image components by using the PCA (i.e. principal component analysis) technique, as the principle of generating the extended APs (EAPs) in [3].
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Kernel-based learning on hierarchical image representations : applications to remote sensing data classification

Kernel-based learning on hierarchical image representations : applications to remote sensing data classification

Figure 3.18: Classification accuracy w.r.t. different maximum considered subpath lengths P. SBoSK is computed on the Strasbourg Spot-4 image with D = 4096. known techniques for spatial/spectral remote sensing image classification. Spatial-spectral kernel [61] has been introduced to take into account pixel spectral value and spatial infor- mation through accessing the nesting region. We thus implement spatial-spectral kernel based on the multiscale segmentation commonly used in this paper, and select the best level (determined by a cross-validation strategy) to extract spatial information. Attribute profile [48] is considered as one of the most powerful techniques to describe image content through context feature. We use full multi-spectral bands with automatic level selection for the area attribute and standard deviation attribute as detailed in [75]. Stacked vector was adopted in [96, 25, 113], and relies on features extracted from a hierarchical representation. We use a Gaussian kernel with stacked vector that concatenates all nodes from ascending paths gen- erated from our multiscale segmentation. The comparison is done by randomly choosing n = [ 50, 100, 200, 400 ] samples for training and the rest for testing. The classification accu- racies with different methods are shown in Tab. 3.3. Three common accuracy assessment measures in the remote sensing community [15, 40] are reported here: overall accuracy, av-
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Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification

Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification

Fig. 5(a) plots the evolution of the area under ROC curve and pixelwise accuracy in the test set, across iterations. The FCN consistently outperforms the patch-based network. Fig. 5(b) shows ROC curves for the final networks after convergence, the FCN exhibiting the best relation between true and false positive rates. Fig. 4(c-d) depicts some visual results. To further illustrate the benefits of neural networks over other learning approaches we train a support vector machine (SVM) with Gaussian kernel on 1,000 randomly selected pixels of each class. We train on the individual pixel spec- tra without any feature selection. The SVM parameters are selected by 5-fold cross-validation, as commonly performed in remote sensing image classification [10]. As shown by Fig. 4(e), the pixelwise SVM classification often confuses roads with buildings due to the fact that their colors are similar, while neural networks better infer and separate the classes by taking into account the geometry of the context. The accuracy of the SVM on the Boston test dataset is 0.6229 and its area under ROC curve is 0.5193, i.e., significantly lower than with CNNs, as shown in Fig. 5. If we wished to successfully use an SVM for this task, we should design and select spatial features (e.g., texture) and use them as the input to the classifier instead. The amplified fragment in Fig. 2 shows that the border discontinuity artifacts present in the patch-based scheme are absent in our fully convolutional setting. This behaves as expected considering that the issues described in Section III-B are addressed by construction in the new architecture. This confirms that imposing sensible restrictions to the connections of a neural network has a positive impact in the performance. In terms of efficiency the FCN also outperforms the patch- based CNN. At inference time, instead of carrying out the prediction in a small patch basis, the input of the FCN is simply increased to output larger predictions, better benefiting from the GPU parallelization of convolutions. The execution time required to classify the whole Boston 22.5 km 2 test set
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Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles

Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles

ihsen.hedhli.1@ulaval.ca * Correspondence: gabriele.moser@unige.it Abstract: In this paper, a hierarchical probabilistic graphical model is proposed to tackle joint classifi- cation of multiresolution and multisensor remote sensing images of the same scene. This problem is crucial in the study of satellite imagery and jointly involves multiresolution and multisensor image fusion. The proposed framework consists of a hierarchical Markov model with a quadtree structure to model information contained in different spatial scales, a planar Markov model to account for contextual spatial information at each resolution, and decision tree ensembles for pixelwise modeling. This probabilistic graphical model and its topology are especially fit for application to very high resolution (VHR) image data. The theoretical properties of the proposed model are analyzed: the causality of the whole framework is mathematically proved, granting the use of time-efficient infer- ence algorithms such as the marginal posterior mode criterion, which is non-iterative when applied to quadtree structures. This is mostly advantageous for classification methods linked to multiresolution tasks formulated on hierarchical Markov models. Within the proposed framework, two multimodal classification algorithms are developed, that incorporate Markov mesh and spatial Markov chain concepts. The results obtained in the experimental validation conducted with two datasets containing VHR multispectral, panchromatic, and radar satellite images, verify the effectiveness of the proposed framework. The proposed approach is also compared to previous methods that are based on alternate strategies for multimodal fusion.
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Fully Convolutional Neural Networks For Remote Sensing Image Classification

Fully Convolutional Neural Networks For Remote Sensing Image Classification

Fig. 3a plots the evolution of area under ROC curve and pixel-wise accuracy, through the iterations. The FCN consis- tently outperforms the patch-based network. Fig. 3b shows ROC curves for the final networks after training, the FCN ex- hibiting a better relation between true and false positive rates (areas under ROC curves are 0.9922 for FCN and 0.9899 for the patch-based network). Fig. 2c-d depicts visual fragments. To further evaluate the benefits of neural networks over other previous learning approaches we trained a support vec- tor machine (SVM) with Gaussian kernel on 1k randomly selected pixels of each class. Such SVM-based approach is common for remote sensing image classification [1].
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Learning approaches for large-scale remote sensing image classification

Learning approaches for large-scale remote sensing image classification

Chapter 4 Learning iterative processes to enhance classification maps Convolutional neural networks have become one of the most studied methods for pix- elwise image classification, both in natural images and in remote sensing. One of the key concerns is the coarseness of the outputs, since elementary CNNs fail to yield fine- grained high-resolution classification maps. In addition to the eventual imperfection of the training data, this is due to a well-known trade-off between the recognition power of CNNs (to identify objects) and their localization power (to precisely outline them). One tendency to overcome this issue is to design new architectures specifically intended to provide high-resolution outputs. In Chapter 3 we proposed a step forward in this direction, through a network that combined two resolutions of reasoning in its layers. In Chapter 5 we also resume this direction of work. A second popular option is to add a dedicated post-processing module to correct the fuzzy output of the original CNN. The base CNN is used as a rough classifier of the objects’ locations, and then the output classification maps are processed so that the objects better align to real image edges. This step requires to use the original image as guidance to improve the fuzzy maps.
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Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification

Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification

to generalize for all classes. On the other hand, the attribute profile requires setting the thresholds for different attributes in order to achieve good classification results. However, as indicated in [ 55 ], generic strategies for filter parameters’ selection for different attributes are still lacking. Comparing to the Gaussian kernel with stacked vector, SBoSK achieves about 8% classification accuracy improvement for various training sample sizes. Since both kernels rely on the same paths, it demonstrates the superiority of SBoSK for taking into account context features extracted from a hierarchical representation. In fact, the Gaussian kernel with the stacked vector is actually a special case of BoSK with the subpath length equal to the maximum (illustrated in our previous study [ 11 ]). However, structured kernels built only on the largest substructures are usually not robust [ 11 ]. Indeed, larger substructures are often penalized when building the structured kernels [ 35 ]. In our experiment, this superiority is presented in the per-class accuracies for all except two homogeneous classes: water surface and forest areas.
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Classification of remote sensing data with morphological attributes profiles: a decade of advances

Classification of remote sensing data with morphological attributes profiles: a decade of advances

C. Attribute and threshold selection APs possess undoubtedly many desirable practical and theoretical properties that render them particularly suitable for the task of spatial-spectral description in our context. However, they are not without flaws, and the main source of their criticism so far has been in terms of their sensitivity to parameter selection [61]–[63], and by parameters we mean particularly the attributes employed to characterize every tree node, and most importantly the set of associated threshold values. The attribute and threshold selection parameters will be discussed in the subsections III-C1 and III-C2, respectively. 1) Attributes: From a theoretical point of view, any function a : P(E) → R, where P(E) denotes the powerset of E, computable on an arbitrary collection of pixels, can be in fact employed as an attribute for AP construction. In practice, it is used during filtering by comparing a given connected component’s (CC ⊆ E) attribute value against a predetermined threshold in the form of a binary predicate (e.g. in case of area, “is the connected component’s area greater than 300 pixels?”). It thus provides a great degree of freedom as far as the object based analysis of an image is concerned. The pioneering paper of APs [7] has introduced four such attributes: area, moment of inertia, diagonal length of bounding box and standard deviation where the first three describe a geometric property related to the shape of the tree node under study and the last, its statistical pixel intensity distribution.
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Spectral and spatial methods for the classification of urban remote sensing data

Spectral and spatial methods for the classification of urban remote sensing data

5.5.2 Experiment The proposed approach has been tested on real hyperspectral data. The University data set was used, see appendix C. The original image is shown in Fig. 5.9.(a). Three principal components were selected and the morphological profile was built using 10 openings/closings by reconstruction. The image was first classified using the spectral data (103 bands) and then using the EMP (63 bands). Gaussian kernels were used for each experiment. The parameters (C, γ) of the SVM were tuned using five-fold cross validation. The results were combined according to the classification scheme previously defined. The accuracies in terms of classification are listed in Table 5.9. The overall accuracy (OA) is the percentage of correctly classified pixels, whereas the average accuracy (AA) represents the average of the individual class accuracies. The coefficient Kappa is another criterion traditionally used in remote-sensing classification to measure the degree of agreement, and takes into account the correct classification that might have been obtained ’by chance’ by weighting the measured accuracies. Per Class classification accuracy has also been reported. The classification map for the absolute maximum fusion operator is presented in Fig. 5.9.(b).
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Classification of Multisensor and Multiresolution Remote Sensing Images through Hierarchical Markov Random Fields

Classification of Multisensor and Multiresolution Remote Sensing Images through Hierarchical Markov Random Fields

The second proposed method obtained rather high OA and κ on the test set (Table II) and remarkable spatial regula- rity (Fig. 3(h)). Again, poor discrimination was obtained for “containers.” To investigate the capability of the method to exploit the synergy among X-band, C-band, and VNIR data, comparisons with the results obtained by using the Pl´eiades image only by itself or together with either SAR image (Table II and Figs. 3(d), (e), and (f)) were conducted. In all these comparisons, the same quad-tree, MPM, and finite- mixture formulations as in the proposed method were used. The results obtained using only SAR data poorly discriminated the classes and are not reported for brevity. A comparison with a nonparametric pixelwise benchmark was performed by res- ampling the CSK and RS2 images on the 0.5-m grid, stacking them together, and applying an SVM with the same model selection strategy as in Section III-A (Fig. 3(g)). Quantitative analysis on the test set confirmed that the joint use of all three sensors led to substantially higher accuracies as compared to the use of a subset of the input data sources. The use of only the Pl´eiades image allowed “water,” “vegetation,” and “bare soil” to be effectively discriminated but led to poor detection of “urban.” The joint use of this VNIR image and of X-
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Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images

Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images

D. Complexity analysis From a practical perspective, the only difference between AP construction and FP construction is that the former outputs pixel values, while the latter outputs attribute/feature values. As such, in case a single and the same attribute is used both for filtering and description, their computational complexity and feature vector lengths are identical. Nevertheless, FP technique possesses a much higher discrimination capacity, since given an image filtered w.r.t. an attribute t, AP only outputs the resulting pixel gray values that are tightly dependent on the chosen attribute. FP on the other hand, can output the values of the attribute t per pixel, as well as any other arbitrarily selected features; e.g. given an image filtered w.r.t. area, it can output the area, mean, standard deviation, moment of inertia per pixel. Naturally, if one selects to output n properties (features) per CC, this will lead to an additional computational cost w.r.t. AP, but only for the amount required to calculate n − 1 additional
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CLASSIFICATION OF VHR REMOTE SENSING IMAGES USING LOCAL FEATURE-BASED ATTRIBUTE PROFILES

CLASSIFICATION OF VHR REMOTE SENSING IMAGES USING LOCAL FEATURE-BASED ATTRIBUTE PROFILES

2 Institute of Information Technologies - Gebze Technical University, 41400, Kocaeli, Turkey minh-tan.pham@irisa.fr ABSTRACT The present paper introduces an extension of attribute profiles (APs) by extracting their local features. The so-called local feature-based attribute profiles (LFAPs) are expected to provide a better characterization of each APs’ filtered pixel (i.e. APs’ sample) within its neigh- borhood, hence better deal with local texture informa- tion from the image’s content. In this work, LFAP is constructed by extracting some simple first-order statis- tical features of the local patch around each APs’ sample such as mean, standard deviation, range, etc. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In order to evaluate the effectiveness of the proposed technique, supervised classification us- ing Random Forest classifier is performed on the VHR panchromatic Reykjavik image. Experimental results show that LFAPs can considerably improve the classi- fication accuracy of the standard APs and the recently proposed histogram-based APs.
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Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images

Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images

In this paper, a family of parsimonious Gaussian process models is reviewed and 5 additional models are proposed to provide more flexibility to the classifier in the context of hyperspectral image analysis. These models allow to build from a finite set of training samples, a Gaussian mixture model in the kernel feature space, where each covariance matrix is free. They assume that the data of each class live in a specific subspace of the kernel feature space. This assumption reduces the number of parameters needed to estimate the decision function and makes the numerical inversion tractable. A closed-form expression is given for the optimal parameters of the decision function. This work extends the models ini- tially proposed in [18], [19]. In particular, the common noise assumption is relaxed, leading to a new set of models for which the level of noise is specific to each class. Furthermore, a closed-form expression for the estimation of the parame- ters enables a fast estimation of the hyperparameters during the cross-validation step. The contributions of this letter are threefold. 1) The definition of new parsimonious models. 2) A comparison in terms of classification accuracy and processing time of the proposed models with state-of-the-art classifiers of hyperspectral images. 3) A fast cross-validation strategy for learning optimal hyperparameters.
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Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images

Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images

For the other sub-models, p is a fixed parameter given by the user. ˆ 3. EXPERIMENTAL RESULTS In this section, results obtained on one real data set are presented. The data set is the University Area of Pavia, Italy, acquired with the ROSIS-03 sensor. The image has 103 spectral bands (d = 103) and is 610 ×340 pixels, see Figure 1.(a). Nine classes have been de- fined by a photo-interpret for a total of 42776 referenced pixels, see Table 2.(a). 50 pixels for each class have been randomly selected from the samples for the training set, and the remaining set of pix- els has been used for validation. The process has been repeated 50 times, each time a new training set has been generated and the vari- ables have been scaled between -1 and 1. The mean result in terms of overall accuracy (percentage of correctly classified pixels) are re- ported.
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A causal hierarchical Markov framework for the classification of multiresolution and multisensor remote sensing images

A causal hierarchical Markov framework for the classification of multiresolution and multisensor remote sensing images

2.2 The proposed framework 2.2.1 Model assumptions In this paper, we formulate a general framework for the joint fusion of multisensor and mul- tiresolution images in a supervised classification scenario. The key-idea of the proposed framework is to generalize the ap- proaches in (Montaldo et al., 2019a, Montaldo et al., 2019b) by introducing a hierarchical Markov model with respect to a quadtree topology and to an arbitrary total order relation on each layer of the tree. Considering a set of well-registered im- ages acquired by distinct VHR sensors at different spatial resol- utions on the same area, each image is included in a correspond- ing layer of the quadtree. Hence, the resolutions of the input images should be in a power-of-2 relation. This is a restriction, but given the resolutions of current VHR sensors, it can be eas- ily met up to minor resampling (Dikshit, Roy, 1996, Inglada et al., 2007) and appropriate antialiasing filtering (Alparone et al., 2008, Mallat, 2009). The hierarchical model naturally fits the requirements of multiresolution fusion. Furthermore, the relations C, E, and J are assumed to be defined within each planar layer of the quadtree. From a graphical viewpoint, this implies that every site s ∈ S ` of every layer S ` but the root (` = 1, 2, . . . , L) is linked to one parent s − ∈ S `−1 located in the upper layer and to the past neighbors {r ∈ S ` : r
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Deep learning for urban remote sensing

Deep learning for urban remote sensing

It comes out that we can now use such powerful statistical models for various remote sensing tasks: detection, classifi- cation or data fusion. Since the early applications to road detection back in 2010 [18], convolutional networks have been successfully used for classification and dense labeling of aerial imagery. They have defined new state-of-the-art performances and showed the re-use of cross-domain databases is possible to gain and transfer knowledge [20], [9]. New challenges will soon be addressed, such as image registration or 3D data analysis. Serendipity plays a role here: while meta-data for standard decision-making are not always available, the co- existence of various correlated, continuous data allows the training of regression models which give the same output as analytic processes, but faster and with more robustness.
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Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania)

Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania)

2.2 Contribution of Knowledge Databases in Remote Sensing 2.2.1 Knowledge Database Approaches Result from the remote sensing image processing depends of the geographic knowledge formalised in databases or in ontological rules . Results of classification (e.g. land cover maps), segmentation or simulation will have no meaning without expert knowledge that will serve to both enhancement of the model training and improvement of the validation results. The classification accuracy of trees species identification made by airborne hyperspectral sensors was enhanced by the expert knowledge training compared to the learning made from spectral library of in-situ measurements of the same species (i.e. increase of Overall accuracy (O.A.) of up to 20%) (Fig.3) (Ouerghemmi et al, 2018a), the obtained O.A.(s) were < 25% for 11 vegetation species identification using the Spectral Angle Mapper (SAM) classifier. In (Ouerghemmi et al, 2018b), the usefulness of an expert knowledge for vegetation mapping was consolidated, and the mapping performance was enhanced when using a machine learning classifier jointly with the knowledge database, O.A. was up to 46% for 8 vegetation species identification.
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