Landslide mapping from multi-sensor data through improved change detection-based Markov random field

Abstract Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved δNDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data

A Multi Views Approach for Remote Sensing Fusion Based on Spectral, Spatial and Temporal Information

The objectives of this chapter are to contribute to the apprehension of image fusion approaches including concepts definition, techniques ethics and results assessment. It is structured in five sections. Following this introduction, a definition of image fusion provides involved fundamental concepts. Respectively, we explain cases in which image fusion might be useful. Most existing techniques and architectures are reviewed and classified in the third section. In fourth section, we focuses heavily on algorithms based on multi-views approach, we compares and analyses the process model and algorithms including advantages, limitations and applicability of each view. The last part of the chapter summarized the benefits and limitations of a multi-view approach image fusion; it gives some recommendations on the effectiveness and the performance of these methods. These recommendations, based on a comprehensive study and meaningful quantitative metrics, evaluate various proposed views by applying them to various environmental applications with different remotely sensed images coming from different sensors. In the concluding section, we fence the chapter with a summary and recommendations for future researches

Characterizing slope instability kinematics by integrating multi-sensor satellite remote sensing observations

Over the past few decades, the occurrence and intensity of geological hazards, such as landslides, have substantially risen due to various factors, including global climate change, seismic events, rapid urbanization and other anthropogenic activities. Landslide disasters pose a significant risk in both urban and rural areas, resulting in fatalities, infrastructure damages, and economic losses. Nevertheless, conventional ground-based monitoring techniques are often costly, time-consuming, and require considerable resources. Moreover, some landslide incidents occur in remote or hazardous locations, making ground-based observation and field investigation challenging or even impossible. Fortunately, the advancements in spaceborne remote sensing technology have led to the availability of large-scale and high-quality imagery, which can be utilized for various landslide-related applications, including identification, monitoring, analysis, and prediction. This efficient and cost-effective technology allows for remote monitoring and assessment of landslide risks and can significantly contribute to disaster management and mitigation efforts. Consequently, spaceborne remote sensing techniques have become vital for geohazard management in many countries, benefiting society by providing reliable downstream services. However, substantial effort is required to ensure that such benefits are provided. For establishing long-term data archives and reliable analyses, it is essential to maintain consistent and continued use of multi-sensor spaceborne remote sensing techniques. This will enable a more thorough understanding of the physical mechanisms responsible for slope instabilities, leading to better decision-making and development of effective mitigation strategies. Ultimately, this can reduce the impact of landslide hazards on the general public. The present dissertation contributes to this effort from the following perspectives: 1. To obtain a comprehensive understanding of spaceborne remote sensing techniques for landslide monitoring, we integrated multi-sensor methods to monitor the entire life cycle of landslide dynamics. We aimed to comprehend the landslide evolution under complex cascading events by utilizing various spaceborne remote sensing techniques, e.g., the precursory deformation before catastrophic failure, co-failure procedures, and post-failure evolution of slope instability. 2. To address the discrepancies between spaceborne optical and radar imagery, we present a methodology that models four-dimensional (4D) post-failure landslide kinematics using a decaying mathematical model. This approach enables us to represent the stress relaxation for the landslide body dynamics after failure. By employing this methodology, we can overcome the weaknesses of the individual sensor in spaceborne optical and radar imaging. 3. We assessed the effectiveness of a newly designed small dihedral corner reflector for landslide monitoring. The reflector is compatible with both ascending and descending satellite orbits, while it is also suitable for applications with both high-resolution and medium-resolution satellite imagery. Furthermore, although its echoes are not as strong as those of conventional reflectors, the cost of the newly designed reflectors is reduced, with more manageable installation and maintenance. To overcome this limitation, we propose a specific selection strategy based on a probability model to identify the reflectors in satellite images

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

Improved Time Series Land Cover Classification by Missing-Observation-Adaptive Nonlinear Dimensionality Reduction

Dimensionality reduction (DR) is a widely used technique to address the curse of dimensionality when high-dimensional remotely sensed data, such as multi-temporal or hyperspectral imagery, are analyzed. Nonlinear DR algorithms, also referred to as manifold learning algorithms, have been successfully applied to hyperspectral data and provide improved performance compared with linear DR algorithms. However, DR algorithms cannot handle missing data that are common in multi-temporal imagery. In this paper, the Laplacian Eigenmaps (LE) nonlinear DR algorithm was refined for application to multi-temporal satellite data with large proportions of missing data. Refined LE algorithms were applied to 52-week Landsat time series for three study areas in Texas, Kansas and South Dakota that have different amounts of missing data and land cover complexity. A series of random forest classifications were conducted on the refined LE DR bands using varying proportions of training data provided by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL); these classification results were compared with conventional metrics-based random forest classifications. Experimental results show that compared with the metrics approach, higher per-class and overall classification accuracies were obtained using the refined LE DR bands of multispectral reflectance time series, and the number of training samples required to achieve a given degree of classification accuracy was also reduced. The approach of applying the refined LE to multispectral reflectance time series is promising in that it is automated and provides dimensionality-reduced data with desirable classification properties. The implications of this research and possibilities for future algorithm development and application are discussed