5,446 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Synthetic aperture imaging with intensity-only data
We consider imaging the reflectivity of scatterers from intensity-only data
recorded by a single moving transducer that both emits and receives signals,
forming a synthetic aperture. By exploiting frequency illumination diversity,
we obtain multiple intensity measurements at each location, from which we
determine field cross-correlations using an appropriate phase controlled
illumination strategy and the inner product polarization identity. The field
cross-correlations obtained this way do not, however, provide all the missing
phase information because they are determined up to a phase that depends on the
receiver's location. The main result of this paper is an algorithm with which
we recover the field cross-correlations up to a single phase that is common to
all the data measured over the synthetic aperture, so all the data are
synchronized. Thus, we can image coherently with data over all frequencies and
measurement locations as if full phase information was recorded
Recommended from our members
Synthetic Aperture Imaging With Intensity-Only Data.
We consider imaging the reflectivity of scatterers from intensity-only data
recorded by a single moving transducer that both emits and receives signals,
forming a synthetic aperture. By exploiting frequency illumination diversity,
we obtain multiple intensity measurements at each location, from which we
determine field cross-correlations using an appropriate phase controlled
illumination strategy and the inner product polarization identity. The field
cross-correlations obtained this way do not, however, provide all the missing
phase information because they are determined up to a phase that depends on the
receiver's location. The main result of this paper is an algorithm with which
we recover the field cross-correlations up to a single phase that is common to
all the data measured over the synthetic aperture, so all the data are
synchronized. Thus, we can image coherently with data over all frequencies and
measurement locations as if full phase information was recorded
SAVI: Synthetic apertures for long-range, subdiffraction-limited visible imaging using Fourier ptychography
Synthetic aperture radar is a well-known technique for improving resolution in radio imaging. Extending these synthetic aperture techniques to the visible light domain is not straightforward because optical receivers cannot measure phase information. We propose to use macroscopic Fourier ptychography (FP) as a practical means of creating a synthetic aperture for visible imaging to achieve subdiffraction-limited resolution. We demonstrate the first working prototype for macroscopic FP in a reflection imaging geometry that is capable of imaging optically rough objects. In addition, a novel image space denoising regularization is introduced during phase retrieval to reduce the effects of speckle and improve perceptual quality of the recovered high-resolution image. Our approach is validated experimentally where the resolution of various diffuse objects is improved sixfold
Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation
Image correlation remote sensing monitoring techniques are becoming key tools for
providing effective qualitative and quantitative information suitable for natural hazard assessments,
specifically for landslide investigation and monitoring. In recent years, these techniques have
been successfully integrated and shown to be complementary and competitive with more standard
remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry.
The objective of this article is to apply the proposed in-depth calibration and validation analysis,
referred to as the Digital Image Correlation technique, to measure landslide displacement.
The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized
by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS
(Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models
and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide
displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the
landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive
sensitivity analyses and statistics-based processing approaches are used to identify the role of the
background noise that affects the whole dataset. This noise has a directly proportional relationship to
the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy
of the environmental-instrumental background noise evaluation allowed the actual displacement
measurements to be correctly calibrated and validated, thereby leading to a better definition of
the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability
(ranging from 1/10 to 8/10 pixel) for each processed dataset
- …