IEEE Geoscience and Remote Sensing Magazine - June 2016 - 34
(a)
Runway
Airplane
(b)
Residential
River
Ocean
Meadow
Industrial
Bare Soil
(c)
fIgURe 8. (a) The whole image for image annotation. (b) The image ground truth. (c) Example images associated with the eight land-use
categories from the image: 1) runway, 2) airplane, 3) residential, 4) river, 5) ocean, 6) meadow, 7) industrial, and 8) bare soil.
each class, while the remaining images were used for testing, as shown in Table 2.
For the Sydney data set, we trained the RCNet function
using stochastic gradient descent with a batch size of 32, a
momentum of 0.9, a weight decay of 0.0005, and a learning
rate of 0.01. We trained each RCNet for roughly 800 cycles
with the whole training set. The PC environment was the
same as previously mentioned. We also compared the final
classification accuracies for RCNet and the traditional
methods. Table 3 shows the average overall accuracies for
the four methods. The results confirm that using the supervised DL method is an efficient way to increase the RS scene
classification accuracy.
ConCLUsIons And fUtURe WoRK
In this technical tutorial, we have systematically reviewed
the state-of-the-art DL techniques in RS data analysis. The
DL techniques were originally rooted in machine-learning
fields for classification and recognition tasks, and they have
only recently appeared in the geoscience and RS community. From the four perspectives of image preprocessing,
pixel-based classification, target recognition, and scene
understanding, we have found that DL techniques have
had significant successes in the areas of target recognition
and scene understanding, i.e., areas that have been widely
accepted as challenges in recent decades in the RS community because such applications require us to abstract the
high-level semantic information from the bottom-level
june 2016
ieee Geoscience and remote sensing magazine
tABLE 2. tHE tRAINING ANd tESt SAMPLES FOR
tHE SydNEy dAtA SEt.
CLASS
SAMPLES
NO.
NAME
tRAINING
tESt
1.
runway
25
97
2.
airplane
25
16
3.
residential
25
381
4.
river
25
59
5.
ocean
25
133
6.
meadow
25
102
7.
industrial
25
101
8.
Bare soil
25
9
200
898
total
tABLE 3. tHE OVERALL ACCURACIES FOR tHE dIFFERENt
MEtHOdS WItH tHE SydNEy dAtA SEt.
MEtHOd
SPMK
SSC
SSAE
RCNet
accuracy
89.67%
91.33%
92.20%
98.78%
features (usually the raw pixel representation), while the
traditional RS methods of feature describing feature
extraction classification are shallow models, with which
35
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