Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery

This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.

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Fusion Network for Change Detection of High-Resolution Panchromatic Imagery

Applied Sciences ◽

10.3390/app9071441 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1441 ◽

Cited By ~ 4

Author(s):

Wahyu Wiratama ◽

Donggyu Sim

Keyword(s):

Neural Network ◽

High Resolution ◽

Change Detection ◽

Area Under The Curve ◽

Network Architectures ◽

Two Stage ◽

Learning Stage ◽

Decision Algorithm ◽

Network Algorithms ◽

High Level

This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front- and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image- and high-level changes of information, respectively. The fusion network employs single-path and dual-path networks to accomplish low-level and high-level differential detection, respectively. Based on two dual outputs, a two-stage decision algorithm was proposed to efficiently yield the final change detection results. The dual outputs were incorporated into the two-stage decision by operating logical operations. The proposed algorithm was designed to incorporate not only dual network outputs but also neighboring information. In this paper, a new fused loss function was presented to estimate the errors and optimize the proposed network during the learning stage. Based on our experimental evaluation, the proposed method yields a better detection performance than conventional neural network algorithms, with an average area under the curve of 0.9709, percentage correct classification of 99%, and Kappa of 75 for many test datasets.

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An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421590497 ◽

2021 ◽

Author(s):

Gulnaz Alimjan ◽

Yiliyaer Jiaermuhamaiti ◽

Huxidan Jumahong ◽

Shuangling Zhu ◽

Pazilat Nurmamat

Keyword(s):

Change Detection ◽

Detection Algorithm ◽

Convolutional Network ◽

Detection Algorithms ◽

Multiple Dimensions ◽

Change Detection Algorithms ◽

Image Change Detection ◽

Control Loss ◽

The Relationship ◽

Entire Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

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Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

Remote Sensing ◽

10.3390/rs12020311 ◽

2020 ◽

Vol 12 (2) ◽

pp. 311 ◽

Cited By ~ 6

Author(s):

Chun Liu ◽

Doudou Zeng ◽

Hangbin Wu ◽

Yin Wang ◽

Shoujun Jia ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Land Cover Classification ◽

Urban Land ◽

Small Sample ◽

Connectivity Pattern ◽

Feature Maps ◽

Convolutional Network ◽

Urban Land Cover

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.

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3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition

Future Internet ◽

10.3390/fi11020042 ◽

2019 ◽

Vol 11 (2) ◽

pp. 42 ◽

Cited By ~ 5

Author(s):

Sheeraz Arif ◽

Jing Wang ◽

Tehseen Ul Hassan ◽

Zesong Fei

Keyword(s):

Short Term Memory ◽

Research Work ◽

Video Frame ◽

Feature Maps ◽

Convolutional Network ◽

Convolutional Networks ◽

Spatio Temporal ◽

3D Cnn ◽

Public Datasets ◽

Motion Map

Human activity recognition is an active field of research in computer vision with numerous applications. Recently, deep convolutional networks and recurrent neural networks (RNN) have received increasing attention in multimedia studies, and have yielded state-of-the-art results. In this research work, we propose a new framework which intelligently combines 3D-CNN and LSTM networks. First, we integrate discriminative information from a video into a map called a ‘motion map’ by using a deep 3-dimensional convolutional network (C3D). A motion map and the next video frame can be integrated into a new motion map, and this technique can be trained by increasing the training video length iteratively; then, the final acquired network can be used for generating the motion map of the whole video. Next, a linear weighted fusion scheme is used to fuse the network feature maps into spatio-temporal features. Finally, we use a Long-Short-Term-Memory (LSTM) encoder-decoder for final predictions. This method is simple to implement and retains discriminative and dynamic information. The improved results on benchmark public datasets prove the effectiveness and practicability of the proposed method.

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A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer Observations

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2009.2022088 ◽

2009 ◽

Vol 47 (12) ◽

pp. 4125-4131 ◽

Cited By ~ 91

Author(s):

M. Piles ◽

D. Entekhabi ◽

A. Camps

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Change Detection ◽

Detection Algorithm

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Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network

Remote Sensing ◽

10.3390/rs11242970 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2970 ◽

Cited By ~ 3

Author(s):

Ziran Ye ◽

Yongyong Fu ◽

Muye Gan ◽

Jinsong Deng ◽

Alexis Comber ◽

...

Keyword(s):

Neural Network ◽

High Resolution ◽

Spatial Information ◽

Remote Sensing Data ◽

Aerial Imagery ◽

Building Extraction ◽

Feature Maps ◽

Convolutional Network ◽

Wide Range ◽

Very High

Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction.

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End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

Remote Sensing ◽

10.3390/rs11111382 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1382 ◽

Cited By ~ 35

Author(s):

Daifeng Peng ◽

Yongjun Zhang ◽

Haiyan Guan

Keyword(s):

Deep Learning ◽

High Resolution ◽

Change Detection ◽

Land Surface ◽

Satellite Image ◽

Feature Representation ◽

Observation Data ◽

Feature Maps ◽

High Resolution Satellite Images ◽

End To End

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

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