COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks

In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications To Human Settlement Modelling

Due to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.</p

Modeling spatial and temporal textures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 155-161).by Fang Liu.Ph.D

Vegetation, topography and snow melt at the Forest-Tundra Ecotone in arctic Europe: a study using synthetic aperture radar

This research was conducted as part of DART (Dynamic Response of the Forest-Tundra Ecotone to Environmental Change), a four year (1998-2002) European Commission funded international programme of research addressing the potential dynamic response of the (mountain birch) forest-tundra ecotone to environmental change. Satellite remote sensing was used to map landscape scale (lO(^1)-lO(^3) m) patterns of vegetation and spatial dynamics of snow melt at the forest-tundra ecotone at three sites along ca. an 8º latitudinal gradient in the Fermoscandian mountain range. Vegetation at the forest-tundra ecotone was mapped using visible -near infrared (VIR) satellite imagery, with class definition dependent on the timing of the acquisition of imagery (related to highly dynamic vegetation phenology) and spatial variation in the FTE. Multi-temporal spacebome ERS-2 synthetic aperture radar (SAR) was used for mapping snow melt. Comprehensive field measurements of snow properties and meteorological data combined with a physically based snow backscatter model indicated potential for mapping wet snow cover at each site. Significant temporal backscatter signatures enabled a classification algorithm to be developed to map the pattern of snow melt across the forest- tundra ecotone. However, diurnal and seasonal melt-freeze effects relative to the timing of ERS-2 SAR image acquisition effectively reduce the temporal resolution of data. Further, the study sites with large topographic variation and complex vegetative cover, provided a challenging operating environment and problems were identified with the robustness of classification during the later stages of snow melt because of the effects of vegetation. Significant associations were identified between vegetation, topography, and snow melt despite limitations in the snow mapping

Fernandez-Steel Skew Normal Mixture Model dengan Pendekatan Bayesian untuk Segmentasi Citra MRI Tumor Otak

Teknologi citra digital medis yang sering digunakan oleh pakar kesehatan untuk mendeteksi tumor otak pada pasien adalah Magnetic Resonance Imaging (MRI). Kesulitan dalam mengolah citra digital hasil MRI adalah memisahkan Region of Interest (ROI) dengan objek lain, sehingga perlu dilakukan segmentasi citra. Segmentasi citra dapat dilakukan dengan clustering. Metode clustering yang sering digunakan untuk segmentasi citra adalah Gaussian Mixture Model (GMM). Namun terda-pat kelemahan dari distribusi Gaussian, yaitu sifatnya yang berekor pendek dan simetris sehingga jika memerlukan model dengan ekor lebih panjang dapat didekati dengan banyak komponen distribusi Gaussian dalam membentuk mixture model. Hal tersebut mengakibatkan sifat parsimoni model kurang terjaga. Selain itu, pada kenyataannya histogram citra MRI tumor otak mengindikasikan adanya skewness. Oleh karena itu, alternatif dari permasalahan tersebut dengan menggunakan distribusi Neo-Normal. Pada penelitian ini dilakukan segmentasi citra MRI untuk mendeteksi lokasi tumor otak menggunakan Fernandez-Steel Skew Normal (FSSN) Mixture Model dengan Pendekatan Bayesian. Distribusi FSSN merupakan salah satu distribusi Neo-Normal yang membentuk distribusi Gaussian maupun Student's t yang dapat stabil dalam modus distribusinya. Pendekatan Bayesian digunakan karena pendekatan statistika klasik untuk estimasi parameter distribusi FSSN sangatlah rumit dan kompleks untuk diimplementasikan secara numerik. Hasil analisis menunjukkan bahwa distribusi FSSN lebih mampu merepresentasikan citra MRI tumor otak serta model yang didapatkan untuk segmentasi citra MRI tumor otak lebih parsimoni dibandingkan GMM. ==================================================================Medical digital imaging technology that is often used by health professionals to detect brain tumors in patients is Magnetic Resonance Imaging (MRI). Difficulty in processing digital image of the MRI is to identifying the separating Region of Interest (ROI) with other objects, so image segmentation is needed. Image segmentation can be done by clustering. Clustering method which is often used for image segmentation is the Gaussian Mixture Model (GMM). GMM has started to be abandoned because, in reality, the symmetric distribution approach is less able to explain the MRI data pattern. In addition, the use of symmetric distribution cannot compete for the model parsimony of an asymmetric distribution to model the long and heavy tail pattern of data. It needs more components in GMM. Therefore, an alternative to these problems is to employ the Neo-Normal distribution. Neo-Normal distribution is a relaxation of normality that is more adaptive to various characteristics of data than the Gaussian distribution. In this research, MRI image segmentation was performed to detect the location of brain tumors using Fernandez-Steel Skew Normal (FSSN) Mixture Model with Bayesian Approach. The FSSN distribution is one of the Neo-Normal distributions that can be skewed adaptively but still stable in its mode. Bayesian approach is used because the classical statistical approach for estimating FSSN distribution parameters is very complex to be implemented numerically. The results indicate that FSSN mixture model has a better performance to represent the data pattern of brain tumor MRI, more parsimony, and able to detect the brain tumor more precisely than the original GMM approach

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.Comment: 82 pages,5 figure