4 research outputs found
Change Detection from SAR Images Based on Deformable Residual Convolutional Neural Networks
Convolutional neural networks (CNN) have made great progress for synthetic
aperture radar (SAR) images change detection. However, sampling locations of
traditional convolutional kernels are fixed and cannot be changed according to
the actual structure of the SAR images. Besides, objects may appear with
different sizes in natural scenes, which requires the network to have stronger
multi-scale representation ability. In this paper, a novel
\underline{D}eformable \underline{R}esidual Convolutional Neural
\underline{N}etwork (DRNet) is designed for SAR images change detection. First,
the proposed DRNet introduces the deformable convolutional sampling locations,
and the shape of convolutional kernel can be adaptively adjusted according to
the actual structure of ground objects. To create the deformable sampling
locations, 2-D offsets are calculated for each pixel according to the spatial
information of the input images. Then the sampling location of pixels can
adaptively reflect the spatial structure of the input images. Moreover, we
proposed a novel pooling module replacing the vanilla pooling to utilize
multi-scale information effectively, by constructing hierarchical residual-like
connections within one pooling layer, which improve the multi-scale
representation ability at a granular level. Experimental results on three real
SAR datasets demonstrate the effectiveness of the proposed DRNet.Comment: Accepted by ACM Multimedia Asia 202
Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks
Very-high-resolution (VHR) images can provide abundant ground details and
spatial geometric information. Change detection in multi-temporal VHR images
plays a significant role in urban expansion and area internal change analysis.
Nevertheless, traditional change detection methods can neither take full
advantage of spatial context information nor cope with the complex internal
heterogeneity of VHR images. In this paper, a powerful feature extraction model
entitled multi-scale feature convolution unit (MFCU) is adopted for change
detection in multi-temporal VHR images. MFCU can extract multi-scale
spatial-spectral features in the same layer. Based on the unit two novel deep
siamese convolutional neural networks, called as deep siamese multi-scale
convolutional network (DSMS-CN) and deep siamese multi-scale fully
convolutional network (DSMS-FCN), are designed for unsupervised and supervised
change detection, respectively. For unsupervised change detection, an automatic
pre-classification is implemented to obtain reliable training samples, then
DSMS-CN fits the statistical distribution of changed and unchanged areas from
selected training samples through MFCU modules and deep siamese architecture.
For supervised change detection, the end-to-end deep fully convolutional
network DSMS-FCN is trained in any size of multi-temporal VHR images, and
directly outputs the binary change map. In addition, for the purpose of solving
the inaccurate localization problem, the fully connected conditional random
field (FC-CRF) is combined with DSMS-FCN to refine the results. The
experimental results with challenging data sets confirm that the two proposed
architectures perform better than the state-of-the-art methods
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework
Convolutional neural networks (CNNs) have been widely used to improve the
accuracy of polarimetric synthetic aperture radar (PolSAR) image
classification. However, in most studies, the difference between PolSAR images
and optical images is rarely considered. Most of the existing CNNs are not
tailored for the task of PolSAR image classification, in which complex-valued
PolSAR data have been simply equated to real-valued data to fit the optical
image processing architectures and avoid complex-valued operations. This is one
of the reasons CNNs unable to perform their full capabilities in PolSAR
classification. To solve the above problem, the objective of this paper is to
develop a tailored CNN framework for PolSAR image classification, which can be
implemented from two aspects: Seeking a better form of PolSAR data as the input
of CNNs and building matched CNN architectures based on the proposed input
form. In this paper, considering the properties of complex-valued numbers,
amplitude and phase of complex-valued PolSAR data are extracted as the input
for the first time to maintain the integrity of original information while
avoiding immature complex-valued operations. Then, a multi-task CNN (MCNN)
architecture is proposed to match the improved input form and achieve better
classification results. Furthermore, depthwise separable convolution is
introduced to the proposed architecture in order to better extract information
from the phase information. Experiments on three PolSAR benchmark datasets not
only prove that using amplitude and phase as the input do contribute to the
improvement of PolSAR classification, but also verify the adaptability between
the improved input form and the well-designed architectures
Naive Gabor Networks for Hyperspectral Image Classification
Recently, many convolutional neural network (CNN) methods have been designed
for hyperspectral image (HSI) classification since CNNs are able to produce
good representations of data, which greatly benefits from a huge number of
parameters. However, solving such a high-dimensional optimization problem often
requires a large amount of training samples in order to avoid overfitting.
Additionally, it is a typical non-convex problem affected by many local minima
and flat regions. To address these problems, in this paper, we introduce naive
Gabor Networks or Gabor-Nets which, for the first time in the literature,
design and learn CNN kernels strictly in the form of Gabor filters, aiming to
reduce the number of involved parameters and constrain the solution space, and
hence improve the performances of CNNs. Specifically, we develop an innovative
phase-induced Gabor kernel, which is trickily designed to perform the Gabor
feature learning via a linear combination of local low-frequency and
high-frequency components of data controlled by the kernel phase. With the
phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to
automatically adapt to the local harmonic characteristics of the HSI data and
thus yields more representative harmonic features. Also, this kernel can
fulfill the traditional complex-valued Gabor filtering in a real-valued manner,
hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our
newly developed Gabor-Nets on three well-known HSIs, suggesting that our
proposed Gabor-Nets can significantly improve the performance of CNNs,
particularly with a small training set.Comment: This paper has been accepted by IEEE TNNL