scholarly journals Change detection based on dimension reduction SLIC and image matching for remote sensing images

2021 ◽  
Vol 2078 (1) ◽  
pp. 012040
Author(s):  
Zhenliang Chang ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Hao Zhuang ◽  
Pan Huang
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Change Detection ◽  
Dimension Reduction ◽  
Error Detection ◽  
Image Matching ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Image Change Detection ◽  
The Impact

Abstract The detection accuracy of traditional change detection algorithms is seriously affected by the low accuracy and high rate of omission, the radiometric correction accuracy, and the classification threshold for difference image. A change detection method based on image segmentation and image matching was proposed for remote sensing images. In this method, super-pixel-based dimension reduction SLIC image segmentation algorithm and SURF algorithms were used. The homogeneous region was used as the segmentation standard, and the homogeneity method was proposed to suppress the impact of inconsistent image segmentation on the change detection results. The experimental results show that this method improves the accuracy of remote sensing image change detection, has good robustness to the problem of redundant data, significantly reduces the error detection rate of image change detection, and can effectively accelerate the speed of change detection.

scholarly journals Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Huang ◽  
Qiuzhi Peng ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Saliency Maps ◽  
Multitemporal Remote Sensing

In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

scholarly journals Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1381 ◽  
Author(s):  
Tao Lei ◽  
Dinghua Xue ◽  
Zhiyong Lv ◽  
Shuying Li ◽  
Yanning Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Change Detection ◽  
Level Set ◽  
Execution Time ◽  
Remote Sensing Images ◽  
Fuzzy C Means ◽  
Landslide Mapping ◽  
Very High

Change detection approaches based on image segmentation are often used for landslide mapping (LM) from very high-resolution (VHR) remote sensing images. However, these approaches usually have two limitations. One is that they are sensitive to thresholds used for image segmentation and require too many parameters. The other one is that the computational complexity of these approaches depends on the image size, and thus they require a long execution time for very high-resolution (VHR) remote sensing images. In this paper, an unsupervised change detection using fast fuzzy c-means clustering (CDFFCM) for LM is proposed. The proposed CDFFCM has two contributions. The first is that we employ a Gaussian pyramid-based fast fuzzy c-means (FCM) clustering algorithm to obtain candidate landslide regions that have a better visual effect due to the utilization of image spatial information. The second is that we use the difference of image structure information instead of grayscale difference to obtain more accurate landslide regions. Three comparative approaches, edge-based level-set (ELSE), region-based level-set (RLSE), and change detection-based Markov random field (CDMRF), and the proposed CDFFCM are evaluated in three true landslide cases in the Lantau area of Hong Kong. The experiments show that the proposed CDFFCM is superior to three comparative approaches in terms of higher accuracy, fewer parameters, and shorter execution time.

A Multi-Level Approach for Change Detection of Buildings Using Satellite Imagery

2018 ◽  
Vol 27 (08) ◽  
pp. 1850031 ◽  
Author(s):  
Md. Abdul Alim Sheikh ◽  
Alok Kole ◽  
Tanmoy Maity
Keyword(s):  
Remote Sensing ◽  
Mutual Information ◽  
Change Detection ◽  
Satellite Imagery ◽  
Image Matching ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Novel Technique ◽  
Multi Level

In this paper a novel technique for building change detection from remote sensing imagery is presented. It includes two main stages: (1) Object-specific discriminative features are extracted using Morphological Building Index (MBI) to automatically detect the existence of buildings in remote sensing images. (2) Pixel-based image matching is measured on the basis of Mutual Information (MI) of the images by Normalized Mutual Information (NMI). Here, the MBI features values are computed for each of the pair images taken over the same region at two different times and then changes in these two MBI images are measured to indicate the building change. MI is estimated locally for all the pixels for image matching and then thresholding is applied for eliminating those pixels which are responsible for strong similarity. Finally, after getting the MBI and NMI images, a further fusion of these two images is done for refinement of the change result. For evaluation purpose, the experiments are carried on QuickBird, IKONOS images and images taken from Google Earth. The results show that the proposed technique can attain acceptable correctness rates above 90% with Overall Accuracy (OA) 89.52%.

scholarly journals A Two-Stage Deep Learning Registration Method for Remote Sensing Images Based on Sub-Image Matching

2021 ◽  
Vol 13 (17) ◽  
pp. 3443
Author(s):  
Yuan Chen ◽  
Jie Jiang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Matching ◽  
Google Earth ◽  
Geometric Transformation ◽  
Remote Sensing Images ◽  
Two Stage ◽  
Registration Method ◽  
Transformation Parameters ◽  
The Impact

The registration of multi-temporal remote sensing images with abundant information and complex changes is an important preprocessing step for subsequent applications. This paper presents a novel two-stage deep learning registration method based on sub-image matching. Unlike the conventional registration framework, the proposed network learns the mapping between matched sub-images and the geometric transformation parameters directly. In the first stage, the matching of sub-images (MSI), sub-images cropped from the images are matched through the corresponding heatmaps, which are made of the predicted similarity of each sub-image pairs. The second stage, the estimation of transformation parameters (ETP), a network with weight structure and position embedding estimates the global transformation parameters from the matched pairs. The network can deal with an uncertain number of matched sub-image inputs and reduce the impact of outliers. Furthermore, the sample sharing training strategy and the augmentation based on the bounding rectangle are introduced. We evaluated our method by comparing the conventional and deep learning methods qualitatively and quantitatively on Google Earth, ISPRS, and WHU Building Datasets. The experiments showed that our method obtained the probability of correct keypoints (PCK) of over 99% at α = 0.05 (α: the normalized distance threshold) and achieved a maximum increase of 16.8% at α = 0.01, compared with the latest method. The results demonstrated that our method has good robustness and improved the precision in the registration of optical remote sensing images with great variation.

scholarly journals Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection

2021 ◽  
Vol 13 (10) ◽  
pp. 1894
Author(s):  
Chen Chen ◽  
Hongxiang Ma ◽  
Guorun Yao ◽  
Ning Lv ◽  
Hua Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Change Detection ◽  
Data Augmentation ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Administrative Procedure ◽  
Adversarial Network ◽  
Training Samples ◽  
Perlin Noise

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.

scholarly journals CHANGE DETECTION WITH MULTI-SOURCE DEFECTIVE REMOTE SENSING IMAGES BASED ON EVIDENTIAL FUSION

2016 ◽  
Vol III-7 ◽  
pp. 125-132 ◽  
Author(s):  
Xi Chen ◽  
Jing Li ◽  
Yunfei Zhang ◽  
Liangliang Tao
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Evidence Theory ◽  
Comparison Method ◽  
Basic Belief ◽  
Detection Accuracy ◽  
Single Pair ◽  
Remote Sensing Images ◽  
Barrier Lake

Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the proposed method, post-classification comparison process is firstly performed on multi-source defective images. Then, the classification results of all the images, together with their corresponding confusion matrixes are used to calculate the Basic Belief Assignment (BBA) of each pixel. Further, based on the principle of Dempster-Shafer evidence theory, a BBA redistribution process is introduced to deal with the defective parts of multi-source data. At last, evidential fusion and decision making rules are applied on the pixel level, and the final map of change detection can be derived. The proposed method can finish change detection with data fusion and image completion in one integrated process, which makes use of the complementary and redundant information from the input images. The method is applied to a case study of landslide barrier lake formed in Aug. 3rd, 2014, with a series of multispectral images from different sensors of GF-1 satellite. Result shows that the proposed method can not only complete the defective parts of the input images, but also provide better change detection accuracy than post-classification comparison method with single pair of pre- and post-change images. Subsequent analysis indicates that high conflict degree between evidences is the main source of errors in the result. Finally, some possible reasons that result in evidence conflict on the pixel level are analysed.

scholarly journals CHANGE DETECTION WITH MULTI-SOURCE DEFECTIVE REMOTE SENSING IMAGES BASED ON EVIDENTIAL FUSION

2016 ◽  
Vol III-7 ◽  
pp. 125-132 ◽  
Author(s):  
Xi Chen ◽  
Jing Li ◽  
Yunfei Zhang ◽  
Liangliang Tao
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Evidence Theory ◽  
Comparison Method ◽  
Basic Belief ◽  
Detection Accuracy ◽  
Single Pair ◽  
Remote Sensing Images ◽  
Barrier Lake

Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the proposed method, post-classification comparison process is firstly performed on multi-source defective images. Then, the classification results of all the images, together with their corresponding confusion matrixes are used to calculate the Basic Belief Assignment (BBA) of each pixel. Further, based on the principle of Dempster-Shafer evidence theory, a BBA redistribution process is introduced to deal with the defective parts of multi-source data. At last, evidential fusion and decision making rules are applied on the pixel level, and the final map of change detection can be derived. The proposed method can finish change detection with data fusion and image completion in one integrated process, which makes use of the complementary and redundant information from the input images. The method is applied to a case study of landslide barrier lake formed in Aug. 3rd, 2014, with a series of multispectral images from different sensors of GF-1 satellite. Result shows that the proposed method can not only complete the defective parts of the input images, but also provide better change detection accuracy than post-classification comparison method with single pair of pre- and post-change images. Subsequent analysis indicates that high conflict degree between evidences is the main source of errors in the result. Finally, some possible reasons that result in evidence conflict on the pixel level are analysed.

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