AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (α) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) Hα, (2) HαSpan, (3) HαA, (4) HαASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that HαASpan (91.43 % for SVM, 92.25 % for RF and 90.55 % for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25 % by RF and HαASpan while lowest classification accuracy was obtained as 66.99 % by NB and Hα. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification

A Novel Phenology Based Feature Subset Selection Technique Using Random Forest for Multitemporal PolSAR Crop Classification

Feature selection techniques intent to select a subset of features that minimizes redundancy and maximizes relevancy for classification problems in machine learning. Standard methods for feature selection in machine learning seldom take into account the domain knowledge associated with the data. Multitemporal crop classification studies with full-polarimetric synthetic aperture radar (PolSAR) data ought to consider the changes in the scattering mechanisms with their phenological growth stages. Hence, it is desirable to incorporate these changes while determining a feature subset for classification. In this study, a random forest (RF) based feature selection technique is proposed that takes into account the changes in the physical scattering mechanism with crop phenological stages for multitemporal PolSAR classification. The partial probability plot, which is an attribute of RF, provides information about the marginal effect of a polarimetric parameter on the desired crop class. Moreover, it is used to identify the specific range of a parameter where the probability of the presence of a particular crop class is high. The proposed technique identifies features that change significantly with crop phenology. The selected features are the ones whose ranges show maximum separation amongst crop classes. Additionally, the feature subset is refined by eliminating correlated features. The E-SAR L-band dataset of the AgriSAR-2006 campaign over the Demmin test site in Germany is used in this study. The classification accuracy using the novel feature selection technique is 99.12%. This is nominally better than using the features obtained from a standard feature selection method used in RF, such as mean decrease Gini (98.73%) and mean decrease accuracy (98.68%) that do not take into account the information based on crop phenology.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), and in part by the European Funds for Regional Development under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P

Advanced techniques for classification of polarimetric synthetic aperture radar data

With various remote sensing technologies to aid Earth Observation, radar-based imaging is one of them gaining major interests due to advances in its imaging techniques in form of syn-thetic aperture radar (SAR) and polarimetry. The majority of radar applications focus on mon-itoring, detecting, and classifying local or global areas of interests to support humans within their efforts of decision-making, analysis, and interpretation of Earth’s environment. This thesis focuses on improving the classification performance and process particularly concerning the application of land use and land cover over polarimetric SAR (PolSAR) data. To achieve this, three contributions are studied related to superior feature description and ad-vanced machine-learning techniques including classifiers, principles, and data exploitation. First, this thesis investigates the application of color features within PolSAR image classi-fication to provide additional discrimination on top of the conventional scattering information and texture features. The color features are extracted over the visual presentation of fully and partially polarimetric SAR data by generation of pseudo color images. Within the experiments, the obtained results demonstrated that with the addition of the considered color features, the achieved classification performances outperformed results with common PolSAR features alone as well as achieved higher classification accuracies compared to the traditional combination of PolSAR and texture features. Second, to address the large-scale learning challenge in PolSAR image classification with the utmost efficiency, this thesis introduces the application of an adaptive and data-driven supervised classification topology called Collective Network of Binary Classifiers, CNBC. This topology incorporates active learning to support human users with the analysis and interpretation of PolSAR data focusing on collections of images, where changes or updates to the existing classifier might be required frequently due to surface, terrain, and object changes as well as certain variations in capturing time and position. Evaluations demonstrated the capabilities of CNBC over an extensive set of experimental results regarding the adaptation and data-driven classification of single as well as collections of PolSAR images. The experimental results verified that the evolutionary classification topology, CNBC, did provide an efficient solution for the problems of scalability and dynamic adaptability allowing both feature space dimensions and the number of terrain classes in PolSAR image collections to vary dynamically. Third, most PolSAR classification problems are undertaken by supervised machine learn-ing, which require manually labeled ground truth data available. To reduce the manual labeling efforts, supervised and unsupervised learning approaches are combined into semi-supervised learning to utilize the huge amount of unlabeled data. The application of semi-supervised learning in this thesis is motivated by ill-posed classification tasks related to the small training size problem. Therefore, this thesis investigates how much ground truth is actually necessary for certain classification problems to achieve satisfactory results in a supervised and semi-supervised learning scenario. To address this, two semi-supervised approaches are proposed by unsupervised extension of the training data and ensemble-based self-training. The evaluations showed that significant speed-ups and improvements in classification performance are achieved. In particular, for a remote sensing application such as PolSAR image classification, it is advantageous to exploit the location-based information from the labeled training data. Each of the developed techniques provides its stand-alone contribution from different viewpoints to improve land use and land cover classification. The introduction of a new fea-ture for better discrimination is independent of the underlying classification algorithms used. The application of the CNBC topology is applicable to various classification problems no matter how the underlying data have been acquired, for example in case of remote sensing data. Moreover, the semi-supervised learning approach tackles the challenge of utilizing the unlabeled data. By combining these techniques for superior feature description and advanced machine-learning techniques exploiting classifier topologies and data, further contributions to polarimetric SAR image classification are made. According to the performance evaluations conducted including visual and numerical assessments, the proposed and investigated tech-niques showed valuable improvements and are able to aid the analysis and interpretation of PolSAR image data. Due to the generic nature of the developed techniques, their applications to other remote sensing data will require only minor adjustments

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research

Wetland mapping and monitoring using polarimetric and interferometric synthetic aperture radar (SAR) data and tools

Wetlands are home to a great variety of flora and fauna species and provide several unique environmental functions, such as controlling floods, improving water-quality, supporting wildlife habitat, and shoreline stabilization. Detailed information on spatial distribution of wetland classes is crucial for sustainable management and resource assessment. Furthermore, hydrological monitoring of wetlands is also important for maintaining and preserving the habitat of various plant and animal species. This thesis investigates the existing knowledge and technological challenges associated with wetland mapping and monitoring and evaluates the limitations of the methodologies that have been developed to date. The study also proposes new methods to improve the characterization of these productive ecosystems using advanced remote sensing (RS) tools and data. Specifically, a comprehensive literature review on wetland monitoring using Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques is provided. The application of the InSAR technique for wetland mapping provides the following advantages: (i) the high sensitivity of interferometric coherence to land cover changes is taken into account and (ii) the exploitation of interferometric coherence for wetland classification further enhances the discrimination between similar wetland classes. A statistical analysis of the interferometric coherence and SAR backscattering variation of Canadian wetlands, which are ignored in the literature, is carried out using multi-temporal, multi-frequency, and multi-polarization SAR data. The study also examines the capability of compact polarimetry (CP) SAR data, which will be collected by the upcoming RADARSAT Constellation Mission (RCM) and will constitute the main source of SAR observation in Canada, for wetland mapping. The research in this dissertation proposes a methodology for wetland classification using the synergistic use of intensity, polarimetry, and interferometry features using a novel classification framework. Finally, this work introduces a novel model based on the deep convolutional neural network (CNN) for wetland classification that can be trained in an end-to-end scheme and is specifically designed for the classification of wetland complexes using polarimetric SAR (PolSAR) imagery. The results of the proposed methods are promising and will significantly contribute to the ongoing efforts of conservation strategies for wetlands and monitoring changes. The approaches presented in this thesis serve as frameworks, progressing towards an operational methodology for mapping wetland complexes in Canada, as well as other wetlands worldwide with similar ecological characteristics

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

A feasibility study on the application of polarimetric decomposition algorithms to the detection of concealed weapons

State of the art security screening technology is not meeting all modern day requirements. There exists a gap in the market for the development of real time systems capable of detecting weapons at standoff ranges. Researchers at the Centre of Sensing and Imaging at Manchester Metropolitan University have developed a radar based screening technology. This technology will offer new security screening capabilities, making it feasible to have portable systems that can detect concealed weapons, with the added advantage of being capable of screening people in a crowd. The next step in the development of this radar system is to investigate the potential of using polarimetric scattering effects to detect concealed weapons, with the aim of improving the robustness and detection capabilities in comparison with the current state-of-the-art systems. This thesis provides a feasibility study in the application of polarimetric decomposition techniques to Concealed Weapon Detection (CWD) and an experimental radar is developed to provide the measurements required for this study. The major outcome of this work is that polarimetric decompositions including the Pauli, Krogager SDH and H-α decompositions have been demonstrated as a viable means of interpreting data for the detection of concealed weapons. This will allow the next generation of radar based weapon detectors to reduce some of the orientation dependency on detection rates as observed in the current state-of-the-art systems. The work presented in this thesis has resulted in a clear understanding of what is required to implement a fully polarimetric radar based weapon detector. The detection of weapons using the developed fully polarimetric radar with the aid of polarimetric decomposition algorithms combined with calibration and signal-processing algorithms has been demonstrated in this thesis

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue