Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. First, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Second, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information-based adaptive fuzzy superpixel (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels’ algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems

Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks

Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known "curse of dimensionality" phenomena. Other approaches based on deep Convolutional Neural Networks (CNNs) have certain limitations and drawbacks, such as high computational complexity, an unfeasibly large training set with ground-truth labels, and special hardware requirements. In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach. The proposed approach has three advantages. First, there is no requirement for an extensive feature extraction process. Second, it is computationally efficient due to utilized compact configurations. In particular, the proposed compact and adaptive CNN model is designed to achieve the maximum classification accuracy with minimum training and computational complexity. This is of considerable importance considering the high costs involved in labelling in PolSAR classification. Finally, the proposed approach can perform classification using smaller window sizes than deep CNNs. Experimental evaluations have been performed over the most commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained overall accuracies range between 92.33 - 99.39% for these benchmark study sites

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification

Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty

A region-based unsupervised segmentation and classification algorithm for polarimetric synthetic aperture radar (SAR) imagery that incorporates region growing and a Markov random field edge strength model is designed and implemented. This algorithm is an extension of the successful Iterative Region Growing with Semantics (IRGS) segmentation and classification algorithm, which was designed for amplitude only SAR imagery, to polarimetric data. Polarimetric IRGS (PolarIRGS) extends IRGS by incorporating a polarimetric feature model based on the Wishart distribution and modifying key steps such as initialization, edge strength computation, and the region growing criterion. Like IRGS, PolarIRGS oversegments an image into regions and employs iterative region growing to reduce the size of the solution search space. The incorporation of an edge penalty in the spatial context model improves segmentation performance by preserving segment boundaries that traditional spatial models will smooth over. Evaluation of PolarIRGS with Flevoland fully polarimetric data shows that it improves upon two other recently published techniques in terms of classification accuracy

Toward Automated Ice-Water Classification on Large Northern Lakes Using RADARSAT-2 Synthetic Aperture Radar Imagery

Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability. These changes are expected to have implications for both human and environmental systems. Additionally, monitoring lake ice cover is required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently the Canadian Ice Service (CIS) monitors lakes using RADARSAT-2 SAR (synthetic aperture radar) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, the Iterative Region Growing using Semantics (IRGS) approach has been employed to perform ice-water classification on 61 RADARSAT-2 scenes of Great Bear Lake and Great Slave Lake over a three year period. This approach first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. These classes are manually labelled by the user, however automated labelling capability is currently in development. An accuracy assessment has been performed on the classification results, comparing outcomes with user-generated reference data as well as the CIS fraction reported at the time of image acquisition. The overall average accuracy of the IRGS method for this dataset is 92%, demonstrating the potential of this semi-automated method to provide detailed and reliable lake ice cover information

Contributions to texture analysis for digital image segmentation

Orientador: Marco Antonio Garcia de Carvalho, Paulo Sérgio Martins PedroDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: Segmentação é o processo de dividir a imagem em regiões ou grupos, que permitam a identificação de objetos ou características.Para isso, a área de Segmentação de Imagens possui uma gama de métodos que segmentam os mais diversos tipos de imagens. Dentre estes, existem os que empregam a análise de padrões de textura; análise esta que viabiliza a interpretação de diversas informações. As informações de textura são extraídas, normalmente, pixel a pixel. Extraí-las de regiões pré-definidas ainda é um desafio e necessita, em alguns casos, adaptar o descritor para este fim. Este trabalho apresenta duas contribuições referentes à análise de textura no processo de segmentação de imagens. A primeira consiste na segmentação de estômatos através da combinação da Transformada Wavelet à trous e a Transformada Watershed. A segunda consiste na aplicação da Matriz de Coocorrência, juntamente com a Transformada Watershed e o Corte Normalizado, para a segmentação de imagens naturais. As bases de dados utilizadas foram, respectivamente, uma base obtida juntamente com o grupo ScianLab da Universidade do Chile e a BSDS500, da Universidade da California-Berkeley. Os resultados em ambas as aplicações foram avaliados pela medida-F, amplamente utilizada na literatura, e comparados a situações que incluem diferentes técnicas para abordar a segmentação e o uso de textura. A performance das técnicas propostas foram bastante promissoras, como na primeira aplicação, com a obtenção de 98% de acurácia na identificação e 70% na segmentação dos estômatos e, para a segunda, na superação da acurácia de outras técnicasAbstract: Image segmentation is the process of splitting images into regions or groups that allows the identification of features and objects. The image segmentation field has several methods that segment the most diverse types of images. Among them, there are those that employ analysis of texture patterns, which facilitates the interpretation of relevant information in an image. Texture information is commonly extracted pixel by pixel from an image. Extraction from predefined regions remains a challenge that often requires the adaptation of texture descriptors. This work offers two contributions to image segmentation using texture analysis. The first is to segment stomata images through the combination of the \textit{à trous} Wavelet and the Watershed transforms. The second consists in the combination of the Gray-Level Coocurrence Matrix, the Watershed Transform and Normalized Cut to segment general images.The datasets were obtained from the ScianLab group (University of Chile) and the BSDS500 (University of California-Berkeley), respectively. The findings in both applications were evaluated by the well-known F-measure. The comparisons include different techniques to approach both segmentation and texture features. The results obtained were promising in both cases. For example, the first application achieved an accuracy of 98% and 70% in the identification and segmentation of stomata structures, respectively. In the second application, the F-measure accuracy outperformed other techniquesMestradoSistemas de Informação e ComunicaçãoMestre em Tecnologi

Automated Remote Sensing Image Interpretation with Limited Labeled Training Data

Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science. Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.4 month

Spaceborne monitoring of Arctic lake ice in a changing climate

Lake ice phenology (timing of ice-on and ice-off) and thickness are changing in response to generally warmer climate conditions at high northern latitudes observed during recent decades. Monitoring changes in the lake ice cover provides valuable evidence in assessing climate variability in the Arctic. To enhance our understanding of the role of lake ice in the Arctic cryosphere and to evaluate the extent to which Arctic lakes have been impacted by the contemporary changing climate, development of a lake ice monitoring system at pan-Arctic scale is needed. While large lakes across the Arctic are currently being monitored through satellite observations, there are extremely sparse and mostly non-existent records tracking the changes in small high-latitude lakes. Employing a combination of spaceborne observations from synthetic aperture radar (SAR) and optical sensors, and simulations from the Canadian Lake Ice Model (CLIMo), this researched aimed to investigate changes in winter ice growth and ice phenology of lakes across the Arctic, focus being given to smaller lakes on the North Slope of Alaska (NSA) and lakes of various sizes in the Canadian Arctic Archipelago (CAA). To determine the changes in the fraction of lakes that freeze to bed (grounded ice) in late winter on the NSA from 1991 to 2011, a time series of ERS-1/2 was analysed. Results show a trend toward increasing floating ice fractions from 1991 to 2011, with the greatest change occurring in April, when the grounded ice fraction declined by 22% (α = 0.01). This finding is in good agreement with the decrease in ice thickness simulated with CLIMo, a lower fraction of lakes frozen to the bed corresponding to a thinner ice cover. Model simulations over the same period as SAR acquisitions (1991-2011) indicate a trend toward thinner ice covers by 18-22 cm (no-snow and 53% snow depth scenarios, α = 0.01). The results emphasize the regime shifts that these lakes are currently undergoing, including shorter ice seasons. The longer-term trends (1950-2011) derived from model simulations show a decrease in the ice cover duration by ~ 24 days consequent to later freeze-up dates by 5.9 days (α = 0.1) and earlier break-up dates by 17.7-18.6 days (α = 0.001). The temporal evolution of backscatter (σ0) from two C-band SAR sensors – Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR Wide Swath – was then used to investigate the potential of high temporal-frequency SAR for determining lake ice phenological events (e.g. freeze onset, melt onset and water-clear-of-ice). Results show that combined SAR observations are generally suitable for detection of important lake ice events timing. However, the wide range of incidence angles and to a certain extent the orbit differences between the observations, the wind effect, particularly during fall freeze-up, the low differences in σ0 during transition from a grounded-ice cover to melt onset of ice in early spring, complicate the detection of lake ice phenological events. In order to order to document the response of ice cover of lakes in the Canadian High Arctic to climate conditions during recent years, a 15-year time series (1997-2011) of RADARSAT-1/2 ScanSAR Wide Swath, ASAR Wide Swath and Landsat acquisitions were analyzed. Results show that earlier melt onset occurred earlier for all 11 polar-desert and polar-oasis lakes that were investigated. With the exception of Lower Murray Lake, all lakes experienced earlier ice-minimum and water-clear-of-ice dates, with greater changes being observed for polar-oasis lakes (9-23.6 days earlier water-clear-of-ice for lakes located in polar oases and 1.6-20 days earlier water-clear-of-ice for polar-desert lakes). Additionally, results suggest that some lakes may be transitioning from a perennial to a seasonal ice regime, with only a few lakes maintaining a perennial ice cover on occasional years. Aside Lake Hazen and Murray Lakes that preserved their ice cover during the summer of 2009, no residual ice was observed on any of the other lakes from 2007 to 2011. This research provides the foundation of a lake-ice monitoring network that can be built on with the newly launched and future SAR and multispectral missions. Additionally, this study shows that in response to warmer climate conditions, Arctic lakes are experiencing regime shifts with overall shorter ice seasons, thinner ice covers, fewer lakes that freeze to the bottom and more lakes that lose the perennial ice cover and experience a seasonal ice regime