IEEE Geoscience and Remote Sensing Magazine - June 2021 - 23

intercept-fringe pattern, and a learning strategy is designed
to self-train CANet in a fully unsupervised manner,
which can produce the clustering result in near-real
time. However, CANet easily oversmooths the interceptfringe
pattern of the noisy pixels, so a postprocessing
procedure of noise-cluster recognition is performed
to assign the noisy pixels, which are masked out from
the PU procedure, into the independent noisy cluster.
Figure 14 depicts the detailed network architecture of
CANet. Subsequently, the final MB PU result is calculated
using the sparse two-stage programming-based MB
processing approach (TSPA)-an extension of the TSPA
method [67]-for processing MB PU on a sparse, irregular
network, which is established from the clustering result
of CANet. The two-stage procedure is jointly referred
to as the CANet-PU method.
Figure 15 compares the MB PU methods using both the
early machine and deep learning techniques. In addition,
Feature Clustering
Component1
Component2
Component3
Component4
Intercept-Fringe
Pattern
Clustering Result
Backpropagation
FIGURE 14. The CANet architecture. The intercept-fringe pattern is given as the input, and the clustering result is output [66].
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FIGURE 15. (a) The reference terrain height. (b) A simulated interferogram with a long normal baseline. (c) The PU result of (b) obtained by ML.
(d) The PU result of (b) obtained by MAP. (e) The PU result of (b) obtained by CA. (f) The PU result of (b) obtained by CANet-PU. (g) The difference
between (a) and (c). (h) The difference between (a) and (d). (i) The difference between (a) and (e). (j) The difference between (a) and (f) [66].
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
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Superpixel Extraction
Convolution1
BN1
ReLu1
Convolution2
BN2
ReLu2
Convolution3
BN3
ReLu3
Convolution4
BN4
Argmax Classification
Superpixel Refinement

IEEE Geoscience and Remote Sensing Magazine - June 2021

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