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Change Detection Using Multi-Source Remotely Sensed Imagery

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 60126

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Prof. Dr. Xin Huang

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: high spatial and hyperspectral remote sensing image processing methods and applications
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Dr. Jiayi Li

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
Interests: machine learning and pattern recognition; hyperspectral remote sensing image processing and urban application
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Dr. Francesca Bovolo

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Fondazione Bruno Kessler, Università degli Studi di Trento, 38122 Trento Area, Italy
Interests: time series analysis; multitemporal image processing; change detection; multitemporal data fusion; multitemporal classification and domain adaptation; trend analysis; regression analysis; damage assessment
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Prof. Dr. Qi Wang

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Guest Editor

School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, P.O. Box 64, Xi'an 710072, China
Interests: remote sensing; image analysis; computer vision; pattern recognition; machine learning
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Dear Colleagues,

The earth’s environments are experiencing unprecedented changes due to intensive urban development, farming, habitat loss, pollution, and climate change. Therefore, a better identification of such changes is imperative. Due to the repeat-pass nature of the sensor platforms, remote sensing imagery seems to be an ideal data source for change detection. With the development of remote sensing imaging techniques, multi-source data, such as optical, SAR, LiDAR, video, which are installed on satellites, aircraft, UAV, ground platforms, are increasingly available. Additionally, owing to large coverage, representation of local knowledge, and open data policies, volunteered and crowdsourcing geographic information data, such as those provided by Open Street Map, Wikimapia, and Google Map Maker, and social network data (provided by Facebook, Twitter, Weibo, etc.), are growing in volume and availability. These freely available geographic datasets can supply ancillary data to assist remote sensing image change detection.

In this context, ever-expanding choices of multi-source data can be considered in the change detection task. Since multi-source data are able to bring complementary information of the same scene, change detection, based on multi-source data, can achieve increased robustness and accuracy compared with those techniques based on a single source. By integrating temporal, spatial, spectral, and semantic information, change detection using multi-source data becomes a promising research subject. However, change detection using multi-source data, especially multi-modal data, remains challenging because of temporal inconsistency, spectral and spatial variations, differences in imaging mechanisms, and difficulty in co-registration. Therefore, the inclusion of a Special Issue in the journal Remote Sensing is timely to promote innovation and improvement of change detection using multi-source data. In this Special Issue, we aim to cover the latest advances and trends in the field of change detection using multi-source remotely sensed imagery.

Prof. Xin Huang
Dr. Jiayi Li
Dr. Francesca Bovolo
Dr. Qi Wang
Guest Editors

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Keywords

Matching and co-registration
Multi-temporal data classification
Multi-source data fusion
Land cover and land use change
Volunteered geographic information data for change detection
3D change analysis
New remote sensing platforms
Image scene change analysis
Time series remote sensing applications
Machine learning for time-series analysis

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Published Papers (7 papers)

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Editorial

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3 pages, 168 KiB

Open AccessEditorial

Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery

by Xin Huang, Jiayi Li, Francesca Bovolo and Qi Wang

Remote Sens. 2019, 11(19), 2216; https://doi.org/10.3390/rs11192216 - 23 Sep 2019

Cited by 1 | Viewed by 2746

Abstract

This special issue hosts papers on change detection technologies and analysis in remote sensing, including multi-source sensors, advanced machine learning technologies for change information mining, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results [...] Read more.

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

Research

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23 pages, 6051 KiB

Open AccessArticle

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

by Daifeng Peng, Yongjun Zhang and Haiyan Guan

Remote Sens. 2019, 11(11), 1382; https://doi.org/10.3390/rs11111382 - 10 Jun 2019

Cited by 588 | Viewed by 27182

Abstract

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. Full article

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

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23 pages, 9448 KiB

Open AccessArticle

Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery

by Dawei Wen, Xin Huang, Anlu Zhang and Xinli Ke

Remote Sens. 2019, 11(7), 763; https://doi.org/10.3390/rs11070763 - 29 Mar 2019

Cited by 18 | Viewed by 5007

Abstract

Inner-city redevelopment is regarded as an effective way to promote land-use efficiency and optimize land-use structure, especially with the establishment of urban growth boundaries in Chinese cities. However, inner-city redevelopment as compared to urban sprawl has been rarely monitored in 2D space, let alone in 3D space. Therefore, in this paper, a novel approach to generate time-series 3D building maps (i.e., building footprint and height) based on high-resolution (2 m) multi-view ZY-3 satellite imagery was proposed. In the proposed method, the building footprint was updated by an object-based image-to-map change detection method, which employed spectral (i.e., HSV and NDVI) and structural features (i.e., morphological building index) to extract non-building and building objects, respectively; building height was estimated automatically through semi-global matching of multi-view images. We applied the proposed method to four representative Chinese megacities, i.e., Beijing, Xi’an, Shanghai, and Wuhan, for the period 2012–2017, and detected building footprints with overall accuracies ranging from 84.84% to 97.60%. The building height estimation was also relatively accurate, with the bias, slope, and root-mean-square error being −0.49–2.30 m, 0.93–1.10 m, and 4.94–7.31 m, respectively. Our results show that the total building coverage decreased over the study period, accompanied by an increase in both area-weighted building height and floor area ratio. In addition, compact low-rise buildings have been replaced by open high-rise buildings in the urban redevelopment process. Moreover, due to the scattered spatial distribution of the redevelopment sites, the local spatial aggregation patterns of building density are unlikely to shift between hotspots (i.e., spatial aggregation of high values) and coldspots (i.e., spatial aggregation of low values). Full article

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

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24 pages, 39089 KiB

Open AccessArticle

Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

by Shiyan Pang, Xiangyun Hu, Mi Zhang, Zhongliang Cai and Fengzhu Liu

Remote Sens. 2019, 11(6), 729; https://doi.org/10.3390/rs11060729 - 26 Mar 2019

Cited by 16 | Viewed by 4372

Abstract

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm. Full article

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

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24 pages, 11979 KiB

Open AccessArticle

Change Detection in Remote Sensing Images Based on Image Mapping and a Deep Capsule Network

by Wenping Ma, Yunta Xiong, Yue Wu, Hui Yang, Xiangrong Zhang and Licheng Jiao

Remote Sens. 2019, 11(6), 626; https://doi.org/10.3390/rs11060626 - 14 Mar 2019

Cited by 49 | Viewed by 7986

Abstract

Homogeneous image change detection research has been well developed, and many methods have been proposed. However, change detection between heterogeneous images is challenging since heterogeneous images are in different domains. Therefore, direct heterogeneous image comparison in the way that we do it is difficult. In this paper, a method for heterogeneous synthetic aperture radar (SAR) image and optical image change detection is proposed, which is based on a pixel-level mapping method and a capsule network with a deep structure. The mapping method proposed transforms an image from one feature space to another feature space. Then, the images can be compared directly in a similarly transformed space. In the mapping process, some image blocks in unchanged areas are selected, and these blocks are only a small part of the image. Then, the weighted parameters are acquired by calculating the Euclidean distances between the pixel to be transformed and the pixels in these blocks. The Euclidean distance calculated according to the weighted coordinates is taken as the pixel gray value in another feature space. The other image is transformed in a similar manner. In the transformed feature space, these images are compared, and the fusion of the two different images is achieved. The two experimental images are input to a capsule network, which has a deep structure. The image fusion result is taken as the training labels. The training samples are selected according to the ratio of the center pixel label and its neighboring pixels’ labels. The capsule network can improve the detection result and suppress noise. Experiments on remote sensing datasets show the final detection results, and the proposed method obtains a satisfactory performance. Full article

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

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17 pages, 4258 KiB

Open AccessArticle

Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis

by Kun Tan, Yusha Zhang, Xue Wang and Yu Chen

Remote Sens. 2019, 11(3), 359; https://doi.org/10.3390/rs11030359 - 11 Feb 2019

Cited by 60 | Viewed by 6117

Abstract

The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods. Full article

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

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14 pages, 6153 KiB

Open AccessArticle

Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

by ZhiYong Lv, TongFei Liu, Jón Atli Benediktsson, Tao Lei and YiLiang Wan

Remote Sens. 2018, 10(11), 1809; https://doi.org/10.3390/rs10111809 - 15 Nov 2018

Cited by 15 | Viewed by 4604

Abstract

To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches. Full article

(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)

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