IEEE Geoscience and Remote Sensing Magazine - June 2017 - 10

information indexes [13]-[15]. Recent works demonstrate
the benefits of using mathematical morphology in modeling
and extracting geometrical information from remote-sensing images for change detection [16]; urban planning [10],
[12], [15]; forest management [17]; and risk assessments [18].
The applications of mathematical morphology are of interest well beyond remote sensing for various applications of
image processing [27]-[29] and computer vision [30], wherever the interpretation and analysis of VHR images/video is
of interest.
Pesaresi and Benediktsson [8] built an MP of an image applying a sequence of opening and closing by reconstruction operators [7] using a structural element (SE) of
predefined and increasing sizes. The approach of [9] extended the method in [8] for hyperspectral data with high
spatial resolution. The resulting method built the MPs
on the first principal components (PCs) extracted from a
hyperspectral image, leading
to the definition of the exThe APs cAn be used To
tended MP (EMP). Bellens
et al. [10] proposed two MPs
exTrAcT feATures ThAT
using disk-shaped and linAre noT only relATed To
ear SEs to model the width
The scAle of The regions
and length of the objects in
in The imAge buT Also
the VHR panchromatic urrelATe To Any meAsures
ban imagery. MPs have been
(e.g., geomeTricAl, Texwidely applied to the analysis
TurAl, And sPecTrAl)
of VHR images, from spatialThAT cAn be comPuTed
characteristics modeling of
on The regions.
panchromatic and multi/hyperspectral images [8]-[10]
to height information extraction of LiDAR data [2], [17], [19], [20] and amplitude and
phase exploitation of SAR [21]-[23]. The efficiency of using MPs to extract additional features for applications (e.g.,
classification and target detection) has been reported in
many recent works [24]-[26].
While MPs are appealing due to their efficiency in
extracting spatial information from VHR remote-sensing
imagery, they have some limitations when it comes to
modeling other geometrical features (e.g., textures). Additionally, SEs are greatly constrained when modeling
concepts of the different characteristics of the spatial information (e.g., size, shape, and homogeneity). Recently,
Dalla Mura et al. [11] proposed morphological APs for reducing the limitations of the MPs. The APs are obtained
by applying a sequence of AFs to a gray-level image [11].
The AFs are operators defined in the mathematical morphology framework that merge connected components at
different levels in the image per some measure computed
on the components (i.e., attributes) [7]. The APs can be
used to extract features that are not only related to the
scale of the regions in the image but also relate to any
measures (e.g., geometrical, textural, and spectral) that
can be computed on the regions. The advantages of APs
over MPs have been reported in the literature, including
10

the advantage that APs allow more geometrical features to
be modeled for the analysis of VHR images [11], [12], [26],
[31], [32]. Applications to multimodal remote-sensing images have recently been reported in [31]-[44].
However, being connected filters, AFs [46]-[48], together with operators based on geodesic reconstruction
[7], [49], [50], suffer the problem of leakage [47] (i.e., regions related to different semantic objects in the image
happen to be connected by spurious links and so are considered to be a single region), which [10] also refers to as
over-reconstruction problems. This phenomenon might lead
to some unexpected results for remote-sensing images.
For example, the size of objects is not accurately estimated
when these objects are spatially connected with others in
the image. In general, smaller objects are wrongly assigned
the attributes of the larger objects connected to them. This
is a significant problem for automated content analysis because, in typical remote-sensing scenes, many objects are
arranged in a complex manner, i.e., roads are connected
to many other objects such as parking lots and buildings.
These connected objects are often wrongly treated as a
single object by using the connected filters (e.g., AFs [11]
and geodesic reconstruction [7]). The situation is even
worse for images with noise that might connect two adjacent but nonconnected regions. Clearly, this leads to poor
classification performances [51].
To overcome the limitation of over-reconstruction
(i.e., the leakage problem) [11], [50], Ronse [45] defined
contraction-based connectivity where one can split pathconnected components into multiple fragments by cutting them at these spurious links between wider regions.
A second-generation connected operator [46] employs a
single mask image to shape the connected components,
both those bounded by the mask and those outside of
it. However, the second-generation connectivity opening with a mask given by an opening or an erosion of the
original distorts the edges of an object, as analyzed in
[52]. Later, Ouzounis and Wilkinson [52] improved the
second-generation connected operator by using an image
partition instead of a single mask. Their proposed r-connectivity allows more flexibility than mask-based secondgeneration connectivity. The approach of [10] proposed
a partial reconstruction for morphological opening and
closing, where one reconstructs a pixel (of an object) with
limited iterations (and not until stability). In our recent
work [51], we proposed a partial reconstruction for AFs to
better model and extract more geometrical information
(including size and shape information) for classifying hyperspectral images. The main characteristic of the partial
reconstruction is that it does not wrongly connect objects
that should remain disconnected, thus better modeling
the spatial information of objects in an image. In addition, with partial reconstruction, the generated profiles
contain a smaller amount of redundant information, because the connected objects disappear when the image is
progressively simplified. The effective performances of
ieee Geoscience and remote sensinG maGazine

june 2017



Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2017

IEEE Geoscience and Remote Sensing Magazine - June 2017 - Cover1
IEEE Geoscience and Remote Sensing Magazine - June 2017 - Cover2
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 1
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 2
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 3
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 4
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 5
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 6
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 7
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 8
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 9
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 10
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 11
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 12
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 13
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 14
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 15
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 16
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 17
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 18
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 19
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 20
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 21
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 22
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 23
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 24
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 25
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 26
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 27
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 28
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 29
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 30
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 31
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 32
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 33
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 34
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 35
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 36
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 37
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 38
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 39
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 40
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 41
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 42
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 43
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 44
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 45
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 46
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 47
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 48
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 49
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 50
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 51
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 52
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 53
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 54
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 55
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 56
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 57
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 58
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 59
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 60
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 61
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 62
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 63
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 64
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 65
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 66
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 67
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 68
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 69
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 70
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 71
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 72
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 73
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 74
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 75
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 76
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 77
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 78
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 79
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 80
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 81
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 82
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 83
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 84
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 85
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 86
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 87
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 88
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 89
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 90
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 91
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 92
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 93
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 94
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 95
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 96
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 97
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 98
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 99
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 100
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 101
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 102
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 103
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 104
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 105
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 106
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 107
IEEE Geoscience and Remote Sensing Magazine - June 2017 - 108
IEEE Geoscience and Remote Sensing Magazine - June 2017 - Cover3
IEEE Geoscience and Remote Sensing Magazine - June 2017 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2023
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2023
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2023
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2023
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2022
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2022
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2022
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2022
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2021
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2021
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2021
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2021
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2020
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2020
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2020
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2020
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2019
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2019
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2019
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2019
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2018
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2018
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2018
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2018
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2017
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2017
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2017
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2017
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2016
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2016
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2016
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2016
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2015
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2015
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2015
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2015
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2014
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2014
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2014
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2014
https://www.nxtbook.com/nxtbooks/ieee/geoscience_december2013
https://www.nxtbook.com/nxtbooks/ieee/geoscience_september2013
https://www.nxtbook.com/nxtbooks/ieee/geoscience_june2013
https://www.nxtbook.com/nxtbooks/ieee/geoscience_march2013
https://www.nxtbookmedia.com