Change Detection in Bitemporal Remote Sensing Images by using Feature Fusion and Fuzzy C-Means

Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

Download Full-text

GRAPH NEURAL NETWORK BASED MULTI-FEATURE FUSION FOR BUILDING CHANGE DETECTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-377-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 377-382

Author(s):

W. Yuan ◽

X. Yuan ◽

Z. Fan ◽

Z. Guo ◽

X. Shi ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Change Detection ◽

Point Cloud ◽

Feature Fusion ◽

Experimental Comparison ◽

3D Point Cloud ◽

Remote Sensing Images ◽

Illumination Variations ◽

Multi Temporal

Abstract. Building Change Detection (BCD) via multi-temporal remote sensing images is essential for various applications such as urban monitoring, urban planning, and disaster assessment. However, most building change detection approaches only extract features from different kinds of remote sensing images for change index determination, which can not determine the insignificant changes of small buildings. Given co-registered multi-temporal remote sensing images, the illumination variations and misregistration errors always lead to inaccurate change detection results. This study investigates the applicability of multi-feature fusion from both directly extract 2D features from remote sensing images and 3D features extracted by the dense image matching (DIM) generated 3D point cloud for accurate building change index generation. This paper introduces a graph neural network (GNN) based end-to-end learning framework for building change detection. The proposed framework includes feature extraction, feature fusion, and change index prediction. It starts with a pre-trained VGG-16 network as a backend and uses U-net architecture with five layers for feature map extraction. The extracted 2D features and 3D features are utilized as input into GNN based feature fusion parts. In the GNN parts, we introduce a flexible context aggregation mechanism based on attention to address the illumination variations and misregistration errors, enabling the framework to reason about the image-based texture information and depth information introduced by DIM generated 3D point cloud jointly. After that, the GNN generated affinity matrix is utilized for change index determination through a Hungarian algorithm. The experiment conducted on a dataset that covered Setagaya-Ku, Tokyo area, shows that the proposed method generated change map achieved the precision of 0.762 and the F1-score of 0.68 at pixel-level. Compared to traditional image-based change detection methods, our approach learns prior over geometrical structure information from the real 3D world, which robust to the misregistration errors. Compared to CNN based methods, the proposed method learns to fuse 2D and 3D features together to represent more comprehensive information for building change index determination. The experimental comparison results demonstrated that the proposed approach outperforms the traditional methods and CNN based methods.

Download Full-text

Building change detection from multi-source remote sensing images based on multi-feature fusion and extreme learning machine

International Journal of Remote Sensing ◽

10.1080/2150704x.2020.1805134 ◽

2020 ◽

Vol 42 (6) ◽

pp. 2246-2257

Author(s):

Chang Wang ◽

Xu Wang

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Extreme Learning Machine ◽

Feature Fusion ◽

Remote Sensing Images ◽

Learning Machine

Download Full-text

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

Remote Sensing ◽

10.3390/rs10091381 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1381 ◽

Cited By ~ 8

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.

Download Full-text

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

Remote Sensing ◽

10.3390/rs13224597 ◽

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.

Download Full-text

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

Remote Sensing ◽

10.3390/rs11161903 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1903 ◽

Cited By ~ 6

Author(s):

Zheng ◽

Cao ◽

Lv ◽

Benediktsson

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Feature Fusion ◽

State Of The Art ◽

Spectral Feature ◽

Spectral Features ◽

Post Processing ◽

Remote Sensing Images ◽

Multi Scale ◽

And Performance

In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial–spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.

Download Full-text