scholarly journals A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

2020 ◽  
Vol 12 (2) ◽  
pp. 205 ◽  
Author(s):  
Moyang Wang ◽  
Kun Tan ◽  
Xiuping Jia ◽  
Xue Wang ◽  
Yu Chen
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Change Detection ◽  
Decision Model ◽  
Multispectral Images ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Siamese Network ◽  
The Difference ◽  
Computational Budget

Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

scholarly journals MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4528
Author(s):  
Xin Yang ◽  
Lei Hu ◽  
Yongmei Zhang ◽  
Yunqing Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Image Features ◽  
Remote Sensing Images ◽  
The Rich ◽  
The Difference ◽  
Basic Network ◽  
And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

scholarly journals Automatic Change Detection Method of Multitemporal Remote Sensing Images Based on 2D-Otsu Algorithm Improved by Firefly Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Huang ◽  
Yuanmin Fang ◽  
Xiaoqing Zuo ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Firefly Algorithm ◽  
Detection Method ◽  
Binary Classification ◽  
Remote Sensing Images ◽  
Threshold Segmentation ◽  
The Difference ◽  
Multitemporal Remote Sensing ◽  
Difference Images

This paper presents a new automatic change detection method of multitemporal remote sensing images based on 2D-Otsu algorithm improved by Firefly algorithm. The proposed method is designed to automatically extract the changing area between two temporal remote sensing images. First, two different temporal remote sensing images were acquired through difference value method of remote sensing images; then, the 2D-Otsu threshold segmentation principles are analyzed and the optimal threshold of 2D-Otsu threshold segmentation method is figured out by using the Firefly algorithm, where the difference images are conducted with binary classification to obtain the changing category and the nonchanging category; finally, the proposed method is used to carry out change detection experiments on the two selected areas, where a variety of methods are compared. Experimental results show that the proposed method can effectively and quickly extract the changing area between the two temporal remote sensing images; thus, it is an effective method of change detection for remote sensing images.

scholarly journals Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4597
Author(s):  
Puhua Chen ◽  
Lei Guo ◽  
Xiangrong Zhang ◽  
Kai Qin ◽  
Wentao Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Urban Development ◽  
Change Detection ◽  
Information Fusion ◽  
Feature Fusion ◽  
Feature Learning ◽  
Remote Sensing Images ◽  
Imaging Processing ◽  
Sensing Applications

Change detection for remote sensing images is an indispensable procedure for many remote sensing applications, such as geological disaster assessment, environmental monitoring, and urban development monitoring. Through this technique, the difference in certain areas after some emergencies can be determined to estimate their influence. Additionally, by analyzing the sequential difference maps, the change tendency can be found to help to predict future changes, such as urban development and environmental pollution. The complex variety of changes and interferential changes caused by imaging processing, such as season, weather and sensors, are critical factors that affect the effectiveness of change detection methods. Recently, there have been many research achievements surrounding this topic, but a perfect solution to all the problems in change detection has not yet been achieved. In this paper, we mainly focus on reducing the influence of imaging processing through the deep neural network technique with limited labeled samples. The attention-guided Siamese fusion network is constructed based on one basic Siamese network for change detection. In contrast to common processing, besides high-level feature fusion, feature fusion is operated during the whole feature extraction process by using an attention information fusion module. This module can not only realize the information fusion of two feature extraction network branches, but also guide the feature learning network to focus on feature channels with high importance. Finally, extensive experiments were performed on three public datasets, which could verify the significance of information fusion and the guidance of the attention mechanism during feature learning in comparison with related methods.

scholarly journals Gated Convolutional Networks for Cloud Removal From Bi-Temporal Remote Sensing Images

2020 ◽  
Vol 12 (20) ◽  
pp. 3427 ◽  
Author(s):  
Peiyu Dai ◽  
Shunping Ji ◽  
Yongjun Zhang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Total Variation ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Training Samples ◽  
Global Consistency ◽  
Cloud Removal ◽  
First Time

Pixels of clouds and cloud shadows in a remote sensing image impact image quality, image interpretation, and subsequent applications. In this paper, we propose a novel cloud removal method based on deep learning that automatically reconstructs the invalid pixels with the auxiliary information from multi-temporal images. Our method’s innovation lies in its feature extraction and loss functions, which reside in a novel gated convolutional network (GCN) instead of a series of common convolutions. It takes the current cloudy image, a recent cloudless image, and the mask of clouds as input, without any requirements of external training samples, to realize a self-training process with clean pixels in the bi-temporal images as natural training samples. In our feature extraction, gated convolutional layers, for the first time, are introduced to discriminate cloudy pixels from clean pixels, which make up for a common convolution layer’s lack of the ability to discriminate. Our multi-level constrained joint loss function, which consists of an image-level loss, a feature-level loss, and a total variation loss, can achieve local and global consistency both in shallow and deep levels of features. The total variation loss is introduced into the deep-learning-based cloud removal task for the first time to eliminate the color and texture discontinuity around cloud outlines needing repair. On the WHU cloud dataset with diverse land cover scenes and different imaging conditions, our experimental results demonstrated that our method consistently reconstructed the cloud and cloud shadow pixels in various remote sensing images and outperformed several mainstream deep-learning-based methods and a conventional method for every indicator by a large margin.

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