2 research outputs found
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
DSDANet: Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection
Change detection (CD) is one of the most vital applications in remote
sensing. Recently, deep learning has achieved promising performance in the CD
task. However, the deep models are task-specific and CD data set bias often
exists, hence it is inevitable that deep CD models would suffer degraded
performance after transferring it from original CD data set to new ones, making
manually label numerous samples in the new data set unavoidable, which costs a
large amount of time and human labor. How to learn a transferable CD model in
the data set with enough labeled data (original domain) but can well detect
changes in another data set without labeled data (target domain)? This is
defined as the cross-domain change detection problem. In this paper, we propose
a novel deep siamese domain adaptation convolutional neural network (DSDANet)
architecture for cross-domain CD. In DSDANet, a siamese convolutional neural
network first extracts spatial-spectral features from multi-temporal images.
Then, through multi-kernel maximum mean discrepancy (MK-MMD), the learned
feature representation is embedded into a reproducing kernel Hilbert space
(RKHS), in which the distribution of two domains can be explicitly matched. By
optimizing the network parameters and kernel coefficients with the source
labeled data and target unlabeled data, DSDANet can learn transferrable feature
representation that can bridge the discrepancy between two domains. To the best
of our knowledge, it is the first time that such a domain adaptation-based deep
network is proposed for CD. The theoretical analysis and experimental results
demonstrate the effectiveness and potential of the proposed method