3 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
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
With the development of Earth observation technology, very-high-resolution
(VHR) image has become an important data source of change detection. Nowadays,
deep learning methods have achieved conspicuous performance in the change
detection of VHR images. Nonetheless, most of the existing change detection
models based on deep learning require annotated training samples. In this
paper, a novel unsupervised model called kernel principal component analysis
(KPCA) convolution is proposed for extracting representative features from
multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep
siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary
and multi-class change detection. In the KPCA-MNet, the high-level
spatial-spectral feature maps are extracted by a deep siamese network
consisting of weight-shared PCA convolution layers. Then, the change
information in the feature difference map is mapped into a 2-D polar domain.
Finally, the change detection results are generated by threshold segmentation
and clustering algorithms. All procedures of KPCA-MNet does not require labeled
data. The theoretical analysis and experimental results demonstrate the
validity, robustness, and potential of the proposed method in two binary change
detection data sets and one multi-class change detection data set