Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images

2019 ◽  
Author(s):  
Hongruixuan Chen ◽  
Chen Wu ◽  
Bo Du ◽  
Liangpei Zhang
Keyword(s):  
Change Detection ◽  
Convolutional Network ◽  
Multi Scale ◽  
Multi Temporal

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

scholarly journals A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images

2020 ◽  
Vol 12 (10) ◽  
pp. 1619 ◽  
Author(s):  
Jia-Wei Chen ◽  
Rongfang Wang ◽  
Fan Ding ◽  
Bo Liu ◽  
Licheng Jiao ◽  
...  
Keyword(s):  
Change Detection ◽  
Sar Image ◽  
Sar Images ◽  
Convolutional Network ◽  
Difference Image ◽  
Multi Scale ◽  
Spatial Pooling ◽  
Testing Dataset ◽  
Image Change Detection ◽  
The Difference

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtain comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases.

Change Detection of Multi-Temporal Remote Sensing Images Based on NSCT and ICA

2013 ◽  
Vol 760-762 ◽  
pp. 1477-1481
Author(s):  
Zhao Qing Cao ◽  
Yi Quan Wu ◽  
Zhi Long Ye ◽  
Hong Wan
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Remote Sensing Images ◽  
Independent Components ◽  
Multi Scale ◽  
Multi Temporal ◽  
Image Separation ◽  
Frequency Components

In order to improve the accuracy and computational efficiency of change detection of multi-temporal remote sensing images, a change detection algorithm based on nonsubsampled contourlet transform (NSCT) and independent component analysis (ICA) is proposed. The flexibility of NSCT in image decomposition and the effectiveness of ICA in image separation are used comprehensively. Firstly, multi-scale decomposition of remote sensing images is performed by NSCT. Then the decomposed low-frequency components and high-frequency components form into partitioned vectors. ICA is carried out for the partitioned vectors and separates mutual independent components. Next the separated components are transformed into image components which include the change image. Finally, change detection result is achieved by threshold segmentation and filtering of the change image. The experimental results show that, compared with the algorithm based on ICA, the algorithm based on wavelet transform and ICA, the proposed algorithm separates change information more effectively and reduces computational complexity. The obtained change image has higher accuracy and stronger robustness to the background.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

scholarly journals Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

2021 ◽  
Vol 13 (12) ◽  
pp. 2425
Author(s):  
Yiheng Cai ◽  
Dan Liu ◽  
Jin Xie ◽  
Jingxian Yang ◽  
Xiangbin Cui ◽  
...  
Keyword(s):  
Deep Learning ◽  
Global Climate ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Sheet Thickness ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Ice Surface ◽  
Layer Extraction

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

scholarly journals An Interactive WebGIS Framework for Coastal Erosion Risk Management

2021 ◽  
Vol 9 (6) ◽  
pp. 567
Author(s):  
Alessandra Capolupo ◽  
Cristina Monterisi ◽  
Alessandra Saponieri ◽  
Fabio Addona ◽  
Leonardo Damiani ◽  
...  
Keyword(s):  
Open Source ◽  
3D Visualization ◽  
Erosion Risk ◽  
Innovative Strategies ◽  
Web Mapping ◽  
Natural Processes ◽  
Multi Scale ◽  
Interactive Interface ◽  
Multi Temporal ◽  
User Friendly

The Italian coastline stretches over about 8350 km, with 3600 km of beaches, representing a significant resource for the country. Natural processes and anthropic interventions keep threatening its morphology, moulding its shape and triggering soil erosion phenomena. Thus, many scholars have been focusing their work on investigating and monitoring shoreline instability. Outcomes of such activities can be largely widespread and shared with expert and non-expert users through Web mapping. This paper describes the performances of a WebGIS prototype designed to disseminate the results of the Italian project Innovative Strategies for the Monitoring and Analysis of Erosion Risk, known as the STIMARE project. While aiming to include the entire national coastline, three study areas along the regional coasts of Puglia and Emilia Romagna have already been implemented as pilot cases. This WebGIS was generated using Free and Open-Source Software for Geographic information systems (FOSS4G). The platform was designed by combining Apache http server, Geoserver, as open-source server and PostgreSQL (with PostGIS extension) as database. Pure javascript libraries OpenLayers and Cesium were implemented to obtain a hybrid 2D and 3D visualization. A user-friendly interactive interface was programmed to help users visualize and download geospatial data in several formats (pdf, kml and shp), in accordance with the European INSPIRE directives, satisfying both multi-temporal and multi-scale perspectives.

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