In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

/pdf/object-oriented-change-detection-for-remote-sensing-images-3im9p4fepe.pdf

Object-oriented change detection for remote sensing images based on multi-scale fusion

In the process of object-oriented change detection,the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysisAiming at this problem,this paper presents a novel object-level change detection method based on multi-scale segmentation and fusionFirst of all,the fine to coarse segmentation is used to obtain initial objects which have different sizes;then,according to the features of the objects,the method of change vector analysis is used to obtain the change detection results of various scalesIn order to improve the accuracy of change detection,this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusionBased on these methods,experiments are done with SPOT5multi-spectral remote sensing imageryCompared with pixel-level change detection methods,the overall accuracy of our method has been improved by nearly 10%,and the experimental results prove the feasibility and effectiveness of the fusion strategies

Object-oriented Change Detection for Remote Sensing Images Based on Multiscale Fusion

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

/pdf/when-to-detect-changes-in-object-based-image-analysis-before-2xl1yccw09.pdf

When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used. 

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Quality monitoring of grid construction process based on satellite remote sensing method can standardize the quality behavior of all parties involved in the engineering construction and related institutions and evaluate the environmental and safety impacts of the grid engineering. Thus, a Siamese-Neural-Network-based approach using 3-channel remote sensing images was proposed to quickly monitor grid construction changes. This approach can localize the changed buildings with the help of previous and post remote sensing images of the interesting regions, classify the changes into increased buildings and decreased buildings, and output the masks reaching pixel-level positioning accuracy. The proposed method was tested and compared with other methods based on 2-channel. It has higher accuracy and better robustness and is suitable for the above-mentioned scenes of changes in grid construction. Through experimental research, it is proved that the use of multi-temporal high-resolution remote sensing data and deep learning methods can not only monitor the overall construction status and progress of the project in a timely manner, realize the monitoring of large-scale passages or site clearance, but also monitor the construction scope in a timely and effective manner. The dynamic changes of the ecological environment and the review and evaluation of the construction workload.

Change Detection of Grid Construction Based on Satellite Remote Sensing Images and Siamese Neural Network

This paper proposed a change detection method,which uses the segments obtained from the image segmentation.Normalized mutual information was introduced into the change detection of remote sensing images.Histogram and joint histogram of segments are used to calculate the normalized mutual information.The best threshold was calculated based on the smallest conditional entropy.Using the normalized mutual information the method compared with the one using correlation coefficient,it shows the normalized mutual information is more suitable for change detection of remote sensing images.

Segment-based Remote Sensing Image Change Detection Using Normalized Mutual Information

This paper proposed a complete change detection method,which uses the segments obtained from the conflation of the GIS data and remote sensing images.The mission of change detection is turned into a two categories division using the similarity of vectors——change and not change.Then,a similarity measure is constructed and the threshold is calculated by data mining.We compare the method using the similarity measure and the related coefficient.It shows that the similarity is more suitable for the change detection.

Segment-based Land Use Change Detection Using the Similarity of Vector

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Segment-based Remote Sensing Image Change Detection Using Normalized Mutual Information

Segment-based Land Use Change Detection Using the Similarity of Vector