10 research outputs found
Online Graph-Based Change Point Detection in Multiband Image Sequences
The automatic detection of changes or anomalies between multispectral and
hyperspectral images collected at different time instants is an active and
challenging research topic. To effectively perform change-point detection in
multitemporal images, it is important to devise techniques that are
computationally efficient for processing large datasets, and that do not
require knowledge about the nature of the changes. In this paper, we introduce
a novel online framework for detecting changes in multitemporal remote sensing
images. Acting on neighboring spectra as adjacent vertices in a graph, this
algorithm focuses on anomalies concurrently activating groups of vertices
corresponding to compact, well-connected and spectrally homogeneous image
regions. It fully benefits from recent advances in graph signal processing to
exploit the characteristics of the data that lie on irregular supports.
Moreover, the graph is estimated directly from the images using superpixel
decomposition algorithms. The learning algorithm is scalable in the sense that
it is efficient and spatially distributed. Experiments illustrate the detection
and localization performance of the method
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
Deep learning-based coastline detection algorithms have begun to outshine
traditional statistical methods in recent years. However, they are usually
trained only as single-purpose models to either segment land and water or
delineate the coastline. In contrast to this, a human annotator will usually
keep a mental map of both segmentation and delineation when performing manual
coastline detection. To take into account this task duality, we therefore
devise a new model to unite these two approaches in a deep learning model. By
taking inspiration from the main building blocks of a semantic segmentation
framework (UNet) and an edge detection framework (HED), both tasks are combined
in a natural way. Training is made efficient by employing deep supervision on
side predictions at multiple resolutions. Finally, a hierarchical attention
mechanism is introduced to adaptively merge these multiscale predictions into
the final model output. The advantages of this approach over other traditional
and deep learning-based methods for coastline detection are demonstrated on a
dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where
coastline detection is notoriously difficult. An implementation of our method
is available at \url{https://github.com/khdlr/HED-UNet}.Comment: This work has been accepted by IEEE TGRS for publication. Copyright
may be transferred without notice, after which this version may no longer be
accessibl
Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses
With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work