Conference Agenda

In recent years, deep neural networks have become a hype tool for various optical and SAR remote sensing image interpretations including object detection, recognition, land use land cover classification, change detection, and multi-temporal image analysis. However, in real-world scenarios, there still are many challenges, such as lack of large annotation data, heterogeneous data transferring, open set recognition, etc. In this talk, we will present our latest efforts to address such problems with the support of the Dragon Project, including： 1) Self-supervised learning for remote sensing image classification and recognition; 2) Heterogeneous SAR domain adaptive object detection; 3) open set recognition and new class discovery for remote sensing images. Moreover, we will introduce an interactive deep learning remote sensing image annotation system and an explainable analysis system for the remote sensing imagery system we are developing.

As an important oceanic physical process, mesoscale eddies play a key role in the processes of ocean mass transport and energy exchange, such as biogeochemical cycles, marine ecosystems, and marine heat balance.

In recent years, these automated ocean eddy identification and tracking algorithms can be divided into two categories: Eulerian- and Lagrangian-based approaches. Based on instantaneous sea surface height (SSH) or velocity field, the major circular structures of mesoscale eddies can be detected, which are called as Eulerian eddies. Previous SSH-based and sea level anomaly-based (SLA-based) methods have shown the best performance due to their ability to avoid extra noise and excess eddy detections. Although SSH-/SLA-based methods worked well at the basin scale, the calculation efficiency decreases distinctly at the global scale, mainly due to the high-order computation complexity on contour iterations. With the improvement of data resolution, existing hardware fails to meet the requirement of long-time-scale global eddy recognition due to the increase of the number of SSH/SLA contours. Thus, based on satellite altimeter data, a highly effective orthogonal parallel algorithm for identifying and tracking global eddies is proposed. Surprisingly, this algorithm is ~100 times faster than the previous SSH-based method on global eddy detection without reducing the accuracy of mesoscale eddy recognition. According to this orthogonal parallel algorithm, the global mesoscale eddy dataset for the past 28 years (1993-2020) was generated, which provides a data foundation for the subsequent study of mesoscale eddies. In terms of the mesoscale eddy dataset, an automatic recognition method of global dipole eddy pairs that consist of two counter-rotating eddies moving together for a period of time within a limited space distance is proposed by using the K–D tree for cutting space. Simultaneously, the transmission modes and characteristics of dipoles are analyzed, including the characteristics of long life, high propagation speed, and entanglement trajectory. In addition, an algorithm named EddyGraph for tracking mesoscale eddy splitting and merging events is proposed and the corresponding data set of eddy trajectories in the Northwest Pacific is available, which would fill the gaps in data sets to support studies on eddy splitting and merging in the Northwest Pacific.

Different from other methods based on instantaneous flow fields, Lagrangian eddies are the cumulative results of the state of the fluid within a given time scale, which can maintain material coherence over the specified time intervals. Firstly, by using the elliptic Lagrangian Coherent Structures, the boundary of a black-hole eddy was extracted based on the data of the geostrophic flow velocity field in the Western Pacific Ocean. Combined with multi-source satellite remote sensing data and in-situ data, the consistency of material transport in the horizontal direction and the coherence of material in the vertical direction of the vortex are analyzed and verified. The results show that the black hole vortex boundary can describe material transport more objectively than the Euler vortex boundary on a longer time scale. Then, the Lagrangian eddies in the western Pacific Ocean are identified and analyzed. By introducing the Niño coefficient, the lag response of the Lagrangian eddy to El Niño is found. Through normalized chlorophyll data, it is observed that Lagrangian eddies can cause chlorophyll aggregation and hole effects. These findings demonstrate the important role of Lagrangian eddies in material transport. Nevertheless, although Lagrangian eddies work well at estimating material transport, the high calculation cost during the integration process has become a bottleneck, especially when the data resolution is improved or the study area is enlarged. Therefore, SLA-based orthogonal parallel detection of global rotationally coherent Lagrangian eddies is built, whose runtime is much faster than that of a previous nonparallel method. Finally, a data set of long-time-scale global Lagrangian eddies is established.

Furthermore, an integrated marine visualization system, named i4Ocean, has been presented. The system is designed and implemented to investigate and study physical marine processes by visualizing and analyzing spatiotemporal marine data. Notably, these actions are realized by providing various GPU-based interaction and visualization techniques for displaying multidimensional data. The system achieves three goals: high visibility, good performance and interactive capabilities. The techniques of z-coordinate calibration and sphere rendering, which restore the most authentic marine environment, provide excellent feedback for oceanographers. The efficient ray sampling technique including a preintegrated transfer function and adaptive sampling methods, increases the rendering efficiency of ocean data. By further introducing a transfer function, users can extract the region of interest in the system and analyze diverse marine phenomena. A data-centric approach was adopted to guide the design of the transfer function by analyzing the scalar field and its properties. The best parameters of the transfer function were obtained to maximize the visibility of important features, which helps to analyze mesoscale eddies of typical ocean phenomena.

Orbital sensors have the capability to provide global repeated observations that can cover the whole world, so they have the potential to observe the earth in a large range, and are widely used in monitoring key phenological events of forest vegetation, monitoring ecosystem environment and other fields. However, due to the limitation of time and space resolution, remote sensing technology faces many challenges in the application of Earth observation. For example, the current satellite image data cannot directly reflect the occurrence and development process of key phenological events. In addition, different methods are used to indirectly estimate ecological indicators to obtain estimation results that are far different, which brings great difficulties to correctly explain and understand the correlation between phenological change and ecosystem and climate change. In a word, the monitoring and research of ecological events based on satellite images need reliable ground reference data, and the lack of relevant ground phenological event observation reference information is the recognized main bottleneck in the field.
In the field of forest phenological monitoring, there are many technical challenges to assess the exact date of phenological events such as the germination and abscission of leaves based on satellite observations. This study aims to explore the feasibility of studying the change characteristics of light reflectivity of vegetation during the occurrence period of key forest phenological events based on the high spatial and temporal resolution long-term forest observation timing of laser radar, and optimizing the monitoring accuracy of satellite phenological events based on the relevant characteristics. The research relies on LiPhe (Lidar Phenological Monitoring) platform and relevant data established by Geospatial Information Research (FGI) of the Finnish National Surveying and Mapping Bureau. In February 2020, LiPhe platform was established in Finland's 111 year old national experimental forest area (Hyytiälä 61°51 ́N,24°17 ́E)。The Liphe platform mainly comprises a Riegl VZ-2000 ground-based lidar scanner (TLS), as well as relevant edge computing and remote control facilities. The scanner is installed on the SMEAR II meteorological observation tower established in 1995, 30 meters from the ground, and the scanning range covers about 10 hectares of forest around the tower. Since April 2020, the scanner will collect data every half an hour and continuously provide point cloud data with high spatial resolution (100 meter distance and 1 cm three-dimensional point distance).

As of October 2021, a year and a half of high spatial and temporal resolution 3D point cloud observation sequence has been obtained.
This study shows the important potential of relevant data as accurate phenological ground observation reference data to support the study of tree level phenological change mechanism and optimize satellite image phenological event monitoring results. With the help of machine learning method, the expected results of the occurrence time of key phenological events in the same forest area based on LiPhe data and satellite data are compared, and the key parameters affecting the prediction of the occurrence time of phenological events are analyzed. The correlation comparison reveals the characteristics of forest light reflectivity changes caused by the dynamic changes of forest structure and the changes of its related physical properties, and demonstrates the optimization role of the relevant characteristics in the interpretation of satellite spectral information phenological events.
In the field of ecological environment quality assessment, scholars have learned the complex and diverse characteristics of the ground ecosystem through in-depth research, and it has natural defects to simply use a single indicator to evaluate the ecological quality. Relying on the advantages of remote sensing data, such as easy access, large area and multi temporal observation, scholars have proposed a variety of comprehensive ecological remote sensing inversion models, coupling a variety of ecological indicators to reflect the quality of the ecological environment. Among them, the RSEI model proposed by Xu Hanqiu couples the four indicators of greenness, humidity, heat and dryness, and uses the principal component analysis method to adaptively determine the weight of the indicators, eliminating the risk of too strong subjective factors of AHP experts' experience to confirm the weight, which has been widely used in the field of ecological remote sensing. This paper studies the ecological environment quality of Shanxi Province in 2013-2019 based on the RSEI model. Specifically, through the Google Earth Engine (GEE) cloud platform coding, the winter RSEI results of 2013-2019 were obtained, and the spatiotemporal differentiation characteristics of the ecological environment in winter in Shanxi Province were studied. The experimental process was mostly based on the GEE cloud platform, which has important research significance for the ecological environment protection in Shanxi Province and even the whole country.