Speckle noise reduction in PolSAR images with binary partition tree

In some remote sensing applications such as PolSAR (Polarimetric Synthetic Aperture Radar), the use of Binary Partition Trees (BPTs) for Speckle Noise filtering schemes is currently gaining interest. In this thesis, a new approach using this representation is investigated: branch filtering. This approach consists in searching for each leaf its ancestors and selecting the one that best represents it, that is, the one that yields the lower error. A potentiality assessment is done to evaluate the margin of improvement that new techniques based on this approach may provide and describe the basic specifications of the algorithms based on it. After that, different new techniques are developed, analysed and compared against the State-of-the-Art. We point out the main strengths and weaknesses of each technique. Our main goal is to understand the behaviour of the filtered data along the BPT branch and interpret how this information can be used in the future for speckle noise reduction in PolSAR images. Finally some conclusions are drawn and some possible future lines of work are exposed and commented.En algunas aplicaciones de teledetección como Polarimetric SAR, el uso de Árboles de Decisión Binarios está ganando interés. En esta tésis se incorpora un nuevo método que usa esta representación: filtraje por ramas. Este método consiste en buscar para cada hoja sus antepasados y seleccionar el mejor nodo como el que de el menor error. Se lleva a cabo un análisis de potencialidad para evaluar el margen de mejora que nuevas técnicas basadas en este método podrían proporcionar y se describen los principios basicos de los algoritmos que se basan en él. Tras esto, se desenvolupan distintas técnicas y se comparan con las del estado del arte. De cada técnica, destacamos sus principales fortalezas y debilidades. Nuestro objetivo principal es entender el comportamiento de los datos filtrados a lo largo de la rama del BPT e interpretar como podemos usar esta información en un futuro para la reducción de ruido especular (speckle) en imágenes PolSAR. Por último, se exponen algunas conclusiones y se presentan y comentan algunas posibles líneas de trabajo futuras.En algunes aplicacions de teledetecció com Polarimetric SAR, l'ús d'Arbres de Particio Binària està guanyant interès. En aquesta tesi, s'investiga un nou mètode que utilitza aquesta representació: filtratge per branques. Aquest mètode consisteix en buscar per cada fulla els seus avantpassats i seleccionar el millor node, és a dir, el que doni un error menor. Es duu a terme un analisi de potencialitat per evaluar el marge de millora que noves tècniques basades en aquest mètode podrien aportar i es descriuen els principis bàsics dels algorismes que s'hi basen. Després, es desenvolupen diverses tècniques i es comparen amb les de l'estat de l'art. Destaquem les principals fortalesses i feblesses de cada tècnica. El nostre principal objectiu és entendre el comportament de les dades filtrades al llarg de la branca del BPT i interpretar com podem utilitzar aquesta informació en un futur per la reducció del soroll especular (speckle) en imatges PolSAR. Per últim s'exposen algunes conclusions i es proposen i comenten possibles noves línies de treball

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

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

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

Classification of Compact Polarimetric Synthetic Aperture Radar Images

The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis. Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping. This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data. The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation

훈련 자료 자동 추출 알고리즘과 기계 학습을 통한 SAR 영상 기반의 선박 탐지

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2021. 2. 김덕진.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vessel’s movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner. As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval. Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.전천후 지구 관측 위성인 SAR를 통한 선박 탐지는 해양 자원의 확보와 해상 안전 보장에 매우 중요한 역할을 한다. 기계 학습 기법의 도입으로 인해 선박을 비롯한 사물 탐지의 정확도 및 효율성이 향상되었으나, 이와 관련된 다수의 연구는 탐지 알고리즘의 개량에 집중되었다. 그러나, 탐지 정확도의 근본적인 향상은 정밀하게 취득된 대량의 훈련자료 없이는 불가능하기에, 본 연구에서는 선박의 실시간 위치, 속도 정보인 AIS 자료를 이용하여 인공 지능 기반의 선박 탐지 알고리즘에 사용될 훈련자료를 자동적으로 취득하는 알고리즘을 제안하였다. 이를 위해 이산적인 AIS 자료를 SAR 영상의 취득시각에 맞추어 정확하게 보간하고, AIS 센서 자체가 가지는 오차를 최소화하였다. 또한, 이동하는 산란체의 시선 속도로 인해 발생하는 도플러 편이 효과를 보정하기 위해 SAR 위성의 상태 벡터를 이용하여 위성과 산란체 사이의 거리를 정밀하게 계산하였다. 이렇게 계산된 AIS 센서의 영상 내의 위치로부터 선박 내 AIS 센서의 배치를 고려하여 선박 탐지 알고리즘의 훈련자료 형식에 맞추어 훈련자료를 취득하고, 어선에 대한 위치, 속도 정보인 VPASS 자료 역시 유사한 방법으로 가공하여 훈련자료를 취득하였다. AIS 자료로부터 취득한 훈련자료는 기존 방법대로 수동 취득한 훈련자료와 함께 인공 지능 기반 사물 탐지 알고리즘을 통해 정확도를 평가하였다. 그 결과, 제시된 알고리즘으로 취득한 훈련 자료는 수동 취득한 훈련 자료 대비 더 높은 탐지 정확도를 보였으며, 이는 기존의 사물 탐지 알고리즘의 평가 지표인 정밀도, 재현율과 F1 score를 통해 진행되었다. 본 연구에서 제안한 훈련자료 자동 취득 기법으로 얻은 선박에 대한 훈련자료는 특히 기존의 선박 탐지 기법으로는 분별이 어려웠던 항만에 인접한 선박과 산란체 주변의 신호에 대한 정확한 분별 결과를 보였다. 본 연구에서는 이와 함께, 선박 탐지 결과와 해당 지역에 대한 AIS 및 VPASS 자료를 이용하여 선박의 미식별성을 판정할 수 있는 가능성 또한 제시하였다.Chapter 1. Introduction - 1 - 1.1 Research Background - 1 - 1.2 Research Objective - 8 - Chapter 2. Data Acquisition - 10 - 2.1 Acquisition of SAR Image Data - 10 - 2.2 Acquisition of AIS and VPASS Information - 20 - Chapter 3. Methodology on Training Data Procurement - 26 - 3.1 Interpolation of Discrete AIS Data - 29 - 3.1.1 Estimation of Target Interpolation Time for Vessels - 29 - 3.1.2 Application of Kalman Filter to AIS Data - 34 - 3.2 Doppler Frequency Shift Correction - 40 - 3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 - 3.2.2 Mitigation of Doppler Frequency Shift - 48 - 3.3 Retrieval of Training Data of Vessels - 53 - 3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 - Chapter 4. Methodology on Object Detection Architecture - 66 - Chapter 5. Results - 74 - 5.1 Assessment on Training Data - 74 - 5.2 Assessment on AIS-based Ship Detection - 79 - 5.3 Assessment on VPASS-based Fishing Boat Detection - 91 - Chapter 6. Discussions - 110 - 6.1 Discussion on AIS-Based Ship Detection - 110 - 6.2 Application on Determining Unclassified Vessels - 116 - Chapter 7. Conclusion - 125 - 국문 요약문 - 128 - Bibliography - 130 -Maste

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringMEngUnrestricte