Multiobjective evolutionary optimization of quadratic Takagi-Sugeno fuzzy rules for remote bathymetry estimation

In this work we tackle the problem of bathymetry estimation using: i) a multispectral optical image of the region of interest, and ii) a set of in situ measurements. The idea is to learn the relation that between the reflectances and the depth using a supervised learning approach. In particular, quadratic Takagi-Sugeno fuzzy rules are used to model this relation. The rule base is optimized by means of a multiobjective evolutionary algorithm. To the best of our knowledge this work represents the first use of a quadratic Takagi-Sugeno fuzzy system optimized by a multiobjective evolutionary algorithm with bounded complexity, i.e., able to control the complexity of the consequent part of second-order fuzzy rules. This model has an outstanding modeling power, without inheriting the drawback of complexity due to the use of quadratic functions (which have complexity that scales quadratically with the number of inputs). This opens the way to the use of the proposed approach even for medium/high dimensional problems, like in the case of hyper-spectral images

Computational intelligence techniques for maritime and coastal remote sensing

The aim of this thesis is to investigate the potential of computational intelligence techniques for some applications in the analysis of remotely sensed multi-spectral images. In particular, two problems are addressed. The first one is the classification of oil spills at sea, while the second one is the estimation of sea bottom depth. In both cases, the exploitation of optical satellite data allows to develop operational tools for easily accessing and monitoring large marine areas, in an efficient and cost effective way. Regarding the oil spill problem, today public opinion is certainly aware of the huge impact that oil tanker accidents and oil rig leaks have on marine and coastal environment. However, it is less known that most of the oil released in our seas cannot be ascribed to accidental spills, but rather to illegal ballast waters discharge, and to pollutant dumping at sea, during routine operations of oil tankers. For this reason, any effort for improving oil spill detection systems is of great importance. So far, Synthetic Aperture Radar (SAR) data have been preferred to multi-spectral data for oil spill detection applications, because of their all-weather and all-day capabilities, while optical images necessitate of clear sky conditions and day-light. On the other hand, many features make an optical approach desirable, such as lower cost and higher revisit time. Moreover, unlike SAR data, optical data are not affected by sea state, and are able to reduce false alarm rate, since they do not suffer from the main false alarm source in SAR data, that is represented by the presence of calm sea regions. In this thesis the problem of oil spill classification is tackled by applying different machine learning techniques to a significant dataset of regions of interest, collected in multi-spectral satellite images, acquired by MODIS sensor. These regions are then classified in one of two possible classes, that are oil spills and look-alikes, where look-alikes include any phenomena other than oil spills (e.g. algal blooms...). Results show that efficient and reliable oil spill classification systems based on optical data are feasible, and could offer a valuable support to the existing satellite-based monitoring systems. The estimation of sea bottom depth from high resolution multi-spectral satellite images is the second major topic of this thesis. The motivations for dealing with this problem arise from the necessity of limiting expensive and time consuming measurement campaigns. Since satellite data allow to quickly analyse large areas, a solution for this issue is to employ intelligent techniques, which, by exploiting a small set of depth measurements, are able to extend bathymetry estimate to a much larger area, covered by a multi-spectral satellite image. Such techniques, once that the training phase has been completed, allow to achieve very accurate results, and, thanks to their generalization capabilities, provide reliable bathymetric maps which cover wide areas. A crucial element is represented by the training dataset, which is built by coupling a number of depth measurements, located in a limited part of the image, with corresponding radiances, acquired by the satellite sensor. A successful estimate essentially depends on how the training dataset resembles the rest of the scene. On the other hand, the result is not affected by model uncertainties and systematic errors, as results from model-based analytic approaches are. In this thesis a neuro-fuzzy technique is applied to two case studies, more precisely, two high resolution multi-spectral images related to the same area, but acquired in different years and in different meteorological conditions. Different situations of in-situ depths availability are considered in the study, and the effect of limited in-situ data availability on performance is evaluated. The effect of both meteorological conditions and training set size reduction on the overall performance is also taken into account. Results outperform previous studies on bathymetry estimation techniques, and allow to give indications on the optimal paths which can be adopted when planning data collection at sea

Retrieval of Lake Erie Water Quality Parameters from Satellite Remote Sensing and Impact on Simulations with a 1-D Lake Model

Lake Erie is a freshwater lake, and the most southern of the Laurentian Great Lakes in North America. It is the smallest by volume, the fourth largest in surface area (25,700 km2), and the shallowest of the Laurentian Great Lakes. The lake’s high productivity and warm weather in its watershed has attracted one-third of the total human population of the Great Lake’s basin. The industrial and agricultural activities of this huge population has caused serious environmental problems for Lake Erie namely harmful algal blooms, dissolved organic/inorganic matters from river inputs, and sediment loadings. If these sorts of water contaminations exceed a certain level, it can seriously influence the lake ecosystem. Hence, an effective and continuous water quality monitoring program is of outmost importance for Lake Erie. The use of Earth observation satellites to improve monitoring of environmental changes in water bodies has been receiving increased attention in recent years. Satellite observations can provide long term spatial and temporal trends of water quality indicators which cannot be achieved through discontinuous conventional point-wise in situ sampling. Different regression-based empirical models have been developed in the literature to derive the water optical properties from a single (or band ratio of) remote sensing reflectance (radiance). In situ measurements are used to build these regressions. The repeated in situ measurements in space and/or time causes clustered and correlated data that violates the assumption of regression models. Considering this correlation in developing regression models was one of the topics examined in this thesis. More complicated semi-analytical models are applied in Case II waters, aiming to distinguish several constituents confounding water-leaving signals more effectively. The MERIS neural network (NN) algorithms are the most widely used among semi-analytical models. The applicability of these algorithms to derive chl-a concentration and Secchi Disk Depth (SDD) in Lake Erie was assessed for the first time in this thesis. Satellite-observations of water turbidity were then coupled with a 1-D lake model to improve its performance on Lake Erie, where the common practice is to use a constant value for water turbidity in the model due to insufficient in situ measurements of water turbidity for lakes globally. In the first chapter, four well-established MERIS NN algorithms to derive chl-a concentration as well as two band-ratio chl-a related indices were evaluated against in situ measurements. The investigated products are those produced by NN algorithms, including Case 2 Regional (C2R), Eutrophic (EU), Free University of Berlin WeW WATER processor (FUB/WeW), and CoastColour (CC) processors, as well as from band-ratio algorithms of fluorescence line height (FLH) and maximum chlorophyll index (MCI). Two approaches were taken to compare and evaluate the performance of these algorithms to predict chl-a concentration after lake-specific calibration of the algorithms. First, all available chl-a matchups, which were collected from different locations on the lake, were evaluated at once. In the second approach, a classification of three optical water types was applied, and the algorithms’ performance was assessed for each type, individually. The results of this chapter show that the FUB/WeW processor outperforms other algorithms when the full matchup data of the lake was used (root mean square error (RMSE) = 1.99 mg m-3, index-of-agreement (I_a) = 0.67). However, the best performing algorithm was different when each water optical type was investigated individually. The findings of this study provide practical and valuable information on the effectiveness of the already existing MERIS-based algorithms to derive the trophic state of Lake Erie, an optically complex lake. Unlike the first chapter, where physically-based and already trained algorithms were implemented to evaluate satellite derived chl-a concentration, in the next chapter, two lake-specific, robust semi-empirical algorithms were developed to derive chl-a and SDD using Linear Mixed Effect (LME) models. LME considers the correlation that exists in the field measurements which have been repeatedly performed in space and time. Each developed algorithm was then employed to investigate the monthly-averaged spatial and temporal trends of chl-a concentration and water turbidity during the period of 2005-2011. SDD was used as the indicator of water turbidity. LME models were developed between the logarithmic scale of the parameters and the band ratio of B7:665 nm to B9:708.75 nm for log10chl-a, and the band ratio of B6:620 nm to B4:510 nm for log10SDD. The models resulted in RMSE of 0.30 for log10chl-a and 0.19 for log10SDD. Maps produced with the two LME models revealed distinct monthly patterns for different regions of the lake that are in agreement with the biogeochemical properties of Lake Erie. Lastly the water turbidity (extinction coefficient; Kd) of Lake Erie was estimated using the globally available satellite-based CC product. The CC-derived Kd product was in a good agreement with the SDD field observations (RMSE=0.74 m-1, mean bias error (MBE)=0.53 m-1, I_a=0.53). CC-derived Kd was then used as input for simulations with the 1-D Freshwater Lake (FLake) model. An annual average constant Kd value calculated from the CC product improved simulation results of lake surface water temperature (LSWT) compared to a “generic” constant value (0.2 m-1) used in previous studies (CC lake-specific yearly average Kd value: RMSE=1.54 ºC, MBE= -0.08 ºC; generic constant Kd value: RMSE=1.76 ºC, MBE= -1.26 ºC). Results suggest that a time-independent, lake-specific, and constant Kd value from CC can improve FLake LSWT simulations with sufficient accuracy. A sensitivity analysis was also conducted to assess the performance of FLake to simulate LSWT, mean water column temperature (MWCT) and mixed layer depth (MLD) using different values of Kd. Results showed that the model is very sensitive to the variations of Kd, particularly when Kd value is below 0.5 m-1. The sensitivity of FLake to Kd variations was more pronounced in simulations of MWCT and MLD. This study shows that a global mapping of the extinction coefficient can be created using satellite-based observations of lakes optical properties to improve the 1-D FLake model. Overall, results from this thesis clearly demonstrate the benefits of remote sensing measurements of water quality parameters (such as chl-a concentration and water turbidity) for lake monitoring. Also, this research shows that the integration of space-borne water clarity (extinction coefficient) measurements into the 1-D FLake model improves simulations of LSWT

Estimating the Concentration of Optically Active Constituents of Sea Water by Takagi-Sugeno Models with Quadratic Rule Consequents

Determining the concentrations of chlorophyll, suspended particulate matter and coloured dissolved organic matter in the sea water is basic to support the monitoring of upwelling phenomena, algae blooms, and changes in the marine ecosystem. Since these concentrations affect the spectral distribution of the solar light back-scattered by the water body, their estimation can be computed by using a set of remotely sensed multispectral measurements of the reflected sunlight. In this paper, the relation between the concentrations of interest and the average subsurface reflectances is modelled by means of a set of second-order Takagi-Sugeno (TS) fuzzy rules. Unlike first-order TS rules, which adopt linear functions as consequent, second-order TS rules exploit quadratic functions, thus improving the modelling capability of the rule in the subspace determined by the antecedent. First, we show how we can build a second-order TS model through a simple transformation, which allows estimating the consequent parameters using standard linear least-squares algorithms, and by adopting one of the most used methods proposed in the literature to generate first-order TS models. Then, we compare first-order and second-order TS models against mean square error and interpretability of rules. We highlight how second-order TS models allow us to achieve better approximation than first-order TS models though maintaining interpretability of the rules. Finally, we show how second-order TS models perform considerably better (the mean square error is lower by two orders of magnitude) than the specific implementations of radial basis function networks and multi-layer perceptron networks used in previous papers for the same application domain