Dimitris G. Stavrakoudis

A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery

This paper proposes the use of a genetic fuzzy-rule-based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks.

Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech Prediction

A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity.

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images

This paper proposes an object-based classification scheme for handling remotely sensed images. The method combines the results of a supervised pixel-based classifier with spatial information extracted from image segmentation. First, pixel-wise classification is implemented by a fuzzy output SVM classifier using spectral and textural features of pixels. This classification results to a set of fuzzy membership maps. Operating on this transformed space, a Genetic Sequential Image Segmentation (GeneSIS) algorithm is next developed to partition the image into homogeneous regions. GeneSIS follows a sequential object extraction approach, whereby at each iteration a single object is extracted by invoking a GA-based object extraction algorithm. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between three fuzzy components: coverage, consistency and smoothness. The final classification map is obtained automatically via segmentation, since each segment is extracted with its own class label. The validity of the proposed method is shown on the land cover classification of three different remote sensing images, with varying number of spectral bands (multispectral/hyperspectral), different spatial resolutions and ground truth cover types. The accuracy results of our approach are favorably compared with the ones obtained by other segmentation-based classification techniques.

A multistage genetic fuzzy classifier for land cover classification from satellite imagery

A linguistic boosted genetic fuzzy classifier (LiBGFC) is proposed in this paper for land cover classification from multispectral images. The LiBGFC is a three-stage process, aiming at effectively tackling the interpretability versus accuracy tradeoff problem. The first stage iteratively generates fuzzy rules, as directed by a boosting algorithm that localizes new rules in uncovered subspaces of the feature space, implicitly preserving the cooperation with previously derived ones. Each rule is able to select the required features, further improving the interpretability of the obtained model. Special provision is taken in the formulation of the fitness function to avoid the creation of redundant rules. A simplification stage follows the first one aiming at further improving the interpretability of the initial rule base, providing a more compact and interpretable solution. Finally, a genetic tuning stage fine tunes the fuzzy sets database improving the classification performance of the obtained model. The LiBGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. The results indicate the effectiveness of the proposed system in handling multidimensional feature spaces, producing easily understandable fuzzy models.

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

Classification of Remotely Sensed Images Using the GeneSIS Fuzzy Segmentation Algorithm

In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the objects extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets.

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through higher-order features is added to the classification process of VHR imagery for burned area mapping.

A recurrent fuzzy neural network for adaptive speech prediction

An enhanced memory TSK-type fuzzy neural network (EM-TRFN) is proposed in this paper, suitable for nonlinear adaptive speech prediction. The feedback links of the network are realized through finite impulse response (FIR) synapses, increasing the depth of the time-series history the network processes. The EM-TRFN is evolved in an on-line manner, with concurrent structure and parameter learning. Simulations on a speech signal prediction problem illustrate the effectiveness of the proposed network, provided by its enhanced temporal capabilities, in grasping the complex dynamic of the speech signal.