IEEE Geoscience and Remote Sensing Magazine - December 2016 - 12

imagery and digital surface models (DSMs) for change detection [146] or hyperspectral imagery and LiDAR for classification tasks [49]. Similar high-level approaches, aimed at
the fusion of InSAR data and other data for improved image
exploitation, have been summarized by Simone et al. [129],
who even consider SAR interferometry itself a case of data fusion, as two images need to be exploited. More sophisticated
data fusion tasks involving InSAR and external auxiliary data
have been proposed by Thiele et al. [142], who discussed the
potential of combining GIS and InSAR data for 3-D building
reconstruction. In contrast, Wegner et al. [166] proposed carrying out this reconstruction task by fusing the InSAR data
with just a single orthophoto. In [142], the ideas were taken
a level lower by employing auxiliary GIS data already during
the InSAR phase filtering step. In analogy, Zhu et al. [180]
suggested incorporating auxiliary GIS data as prior knowledge
into tomographic SAR processing for more accurate three- or
even four-dimensional imaging of urban areas while significantly reducing the number of SAR images required.
Other reasons for data fusion in the SAR-optical fusion
context are the sharpening of low-resolution optical images
by very high-resolution SAR imagery [5], [111] or their exploitation for traffic monitoring [63]. Since the alignment
of very high-resolution SAR and optical data is a nontrivial
task because of strong radiometric and geometric differences
[102], [137], recent fusion approaches have attempted to circumvent these problems by incorporating prior knowledge
in the form of existing 3-D geodata and the simulation of
reference data sets [138]. The resulting data set is then used
for further information extraction, e.g., change detection or
classification [103]. Interestingly, approaches for a stereogrammetric exploitation of SAR and optical data were suggested
for low-resolution sensors and sparse matching situations
two decades ago [13], [108], but these have lost the interest
of the modern data fusion community until recently [184].
Urban areas lie at the center of interest for many remote
sensing researchers. No wonder that a particularly visible application of data fusion is its support for urban mapping in
a variety of scales. Comprehensive and recent reviews of the
research activities in remote sensing data fusion with special
regard for the mapping of urban areas are provided in [44]
and [150]. Among others, an intensively discussed topic on
local scale is the fusion of SAR and optical data for tasks such
as urban surface model generation [147], [154], damage
assessment [19], road network extraction [79], or building
modeling [133], [153]. The latest developments in this area
have extended the mapping of urban areas supported by data
fusion even to the global scale [43], [45], [46], [116].
SINGLE-SENSOR DATA FUSION
Although most of the aforementioned examples aim at fusing data from different sensor types, there is some literature
available about the fusion of single-sensor data from different viewing angles as well as at different resolutions or from
different points in time. For example, so-called multiaspect
SAR data acquired from flexible airborne platforms have
12

been used for optimizing road extraction [59], [60], [118],
[136], [152], 3-D reconstruction of urban surface models
[119], [120], and semantic building recognition [14], [132],
[140], [141], [143], [170]. The lessons learned from these
investigations have already been transferred to the more
limited case of ascending-descending data fusion using satellite-borne sensors, e.g., for building façade reconstruction
[179]. However, the task of data alignment is also a prominent problem in this context, aimed either at the registration
of multiangular SAR imagery directly [33], [93], [122] or of
point clouds generated from multiaspect data sets [48], [162].
Another application of multiangular data is precise StereoSAR positioning [52]. While multiaspect approaches naturally have been investigated mostly within the context of SAR
remote sensing because of its side-looking imaging geometry, multiangular data have also been used in optical remote
sensing, e.g., for improving classification performance [83]
or achieving superresolution [176].
Most of the literature on multiresolution single-sensor
data fusion is found in the previously mentioned field of pan
sharpening. There are, however, a few examples of multiresolution applications with different scopes, such as the fusion of
multifrequency SAR imagery as a preprocessing step in scene
interpretation [128], the exploitation of multiresolution SAR
data for unsupervised change detection [94] and the mapping
of urban areas [115], [116] or multiresolution staring spotlight
and high-resolution spotlight data for SAR tomography [47].
Although the exploitation of multitemporal data is generally
considered part of the data fusion theme complex, change detection techniques have long been a research direction in their
own right within the remote sensing community and shall
not be considered any further in this article [16], [67], [130].
Although not strictly (sensor) data fusion in the context
of this review, a final group of decision-level-based fusion approaches must be mentioned. Here, the fusion task is not applied to the combination of data acquired by different sensors;
instead, only a single input data set is used, and the fusion step
is applied to several preliminary classification results obtained
from this input. Examples of this type of fusion were presented
by [151] for the interpretation of SAR images; by [9] for multispectral, elevation, and multiangular data; by [24] and [41] for
Ikonos imagery; and by [172] for hyperspectral images.
WHAT'S NEXT? FUSION OF STRONGLY
HETEROGENEOUS DATA
Among the most sophisticated fusion tasks is the combination of strongly heterogeneous space-borne, airborne, and
terrestrial data for the development of seamless multiview
and multiresolution Earth-observation systems. In this context, e.g., the automatic georeferencing of satellite images can
be realized by exploiting existing orthophotos made from
aerial images [50] or by registering them to accurately geocoded SAR data [110]. Additional examples are the fusion of
aerial imagery and terrestrial photos for the reconstruction
of building models [69], [160], the fusion of aerial imagery
and vehicle-borne sensor data (e.g., omnidirectional optical
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