IEEE Geoscience and Remote Sensing Magazine - March 2019 - 30

model [204] and the CNN model are constructed based
on the pretrained ImageNet VGG-16 network [205] with
cross entropy loss. The SVM model with radial basis function kernels is adopted for a further comparison. Figure 15
illustrates several classification maps obtained by SVM,
CNN, and FCN with and withThErE ArE sTiLL mAny
out using SMPs.
DOOrs OpEn FOr FUrThEr
Except for the challenge
invEsTiGATiOn, bOTh
of de sig ning a nove l dat a
FrOm ThE ThEOrETicAL
technology for a big data that
combines remote sensing and
AnD AppLicATiOn
social media data, how to expErspEcTivEs, EvEn
ploit the two different types of
ThOUGh ThE FiELD OF
data is another challenging
rEmOTE sEnsinG DATA
problem. In terms of future
FUsiOn is mATUrE.
trends, remote sensing big data
will be utilized for monitoring natural hazards as well as
human-made disasters, such as factory explosions. In addition, remote sensing big data can provide rich information
on the content and location of the scenes for augmented
reality applications, 3D reconstruction, indoor positioning,
and so on.

cOncLUsiOns
The ever-growing availability of data captured by different sensors, coupled with advances in methodological
approaches and computational tools, makes it desirable
to fuse considerably heterogeneous complementary data
sets to increase the efficacy and efficiency of the remotely
sensed data processing approaches with respect to the
problems at hand. The field of multisensor and multitemporal data fusion for remotely sensed imagery is
enormously broad, which makes it challenging to treat it
comprehensively in one literature review. This article focused particularly on advances in multisource and multitemporal data fusion approaches with respect to different research communities because the methods for fusing
different modalities have expanded along different paths
with respect to each research area. In this context, several
vibrant fusion topics, including pansharpening and resolution enhancement, point cloud data fusion, hyperspectral and lidar data fusion, multitemporal data fusion, and
big data and social media, were detailed, and their corresponding challenges and possible future research directions were outlined and discussed.
As demonstrated through the challenges and possible future research noted in each section, there are

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Vegetation

Buildings

Roads

Water

FiGUrE 15. Several classification maps obtained by SVM, CNN, and FCN with or without using SMPs: (a) a test image, (b) segmentation, (c)
FCN + SMPs, (d) FCN, (e) CNN + SMPs, (f) CNN, (g) SVM + SMPs, and (h) SVM.

30

ieee Geoscience and remote sensing magazine

march 2019



IEEE Geoscience and Remote Sensing Magazine - March 2019

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - March 2019

Contents
IEEE Geoscience and Remote Sensing Magazine - March 2019 - Cover1
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IEEE Geoscience and Remote Sensing Magazine - March 2019 - Contents
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