Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles

Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building detection was performed. For detecting building candidates, we calculated feature information that could describe building objects, such as rectangular fit, gray-level co-occurrence matrix (GLCM) homogeneity, and area. Final building detection was then performed considering the location relationship between building objects and their shadows using the Sun’s azimuth angle. Subsequently, building change detection of final building objects was performed based on three methods considering the relationship of the building object properties between the images. First, only overlaying objects between images were considered to detect changes. Second, the size difference between objects according to the sensor’s elevation angle was considered to detect the building changes. Third, the direction between objects according to the sensor’s azimuth angle was analyzed to identify the building changes. To confirm the effectiveness of the proposed object-based building change detection performance, two building density areas were selected as study sites. Site 1 was constructed using a single sensor of KOMPSAT-3 bitemporal images, whereas Site 2 consisted of multi-sensor images of KOMPSAT-3 and unmanned aerial vehicle (UAV). The results from both sites revealed that considering additional shadow information showed more accurate building detection than using feature information only. Furthermore, the results of the three object-based change detections were compared and analyzed according to the characteristics of the study area and the sensors. Accuracy of the proposed object-based change detection results was achieved over the existing building detection methods.

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Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽

10.3390/s21010320 ◽

2021 ◽

Vol 21 (1) ◽

pp. 320

Author(s):

Emilio Guirado ◽

Javier Blanco-Sacristán ◽

Emilio Rodríguez-Caballero ◽

Siham Tabik ◽

Domingo Alcaraz-Segura ◽

...

Keyword(s):

High Resolution ◽

Optical Sensors ◽

Biotic Interactions ◽

Relevant Information ◽

Vegetation Monitoring ◽

Object Based Image Analysis ◽

Object Based ◽

Segmentation Methods ◽

High Resolution Images ◽

Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

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A Multilevel Contextual Approach to Change Detection for very high Resolution Images

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2008.4779688 ◽

2008 ◽

Author(s):

Chunlei Huo ◽

Keming Chen ◽

Zhixin Zhou ◽

Hanqing Lu ◽

Jian Cheng ◽

...

Keyword(s):

High Resolution ◽

Change Detection ◽

Contextual Approach ◽

High Resolution Images ◽

Very High

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Object-based feature extraction and semi-supervised classification for urban change detection using high-resolution remote sensing images

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2015.7326108 ◽

2015 ◽

Author(s):

Bin Hou ◽

Qingjie Liu ◽

Yunhong Wang

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

High Resolution ◽

Change Detection ◽

Supervised Classification ◽

Urban Change ◽

Remote Sensing Images ◽

Object Based

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Unsupervised Land Use/Land Cover Change Detection with Integrated Pixel and Object Based Approaches for High Resolution Remote Sensing Imagery

Earth Observation for Land and Emergency Monitoring ◽

10.1002/9781118793787.ch6 ◽

2017 ◽

pp. 101-116

Author(s):

S. Shrestha ◽

C. Smith ◽

Z. Bochenek

Keyword(s):

Remote Sensing ◽

Land Use ◽

High Resolution ◽

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Land Use Land Cover ◽

Remote Sensing Imagery ◽

Object Based ◽

Land Cover Change Detection

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Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

Remote Sensing ◽

10.3390/rs12060983 ◽

2020 ◽

Vol 12 (6) ◽

pp. 983 ◽

Cited By ~ 4

Author(s):

Youkyung Han ◽

Aisha Javed ◽

Sejung Jung ◽

Sicong Liu

Keyword(s):

High Resolution ◽

Change Detection ◽

Principal Component ◽

Geographic Area ◽

Majority Voting ◽

Dempster Shafer Theory ◽

Object Based ◽

Intensity Images ◽

Shafer Theory ◽

Very High

Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates. CD is divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). Although PBCD is more popular due to its simple algorithms and relatively easy quantitative analysis, applying this method in very high resolution (VHR) images often results in misdetection or noise. Because of this, researchers have focused on extending the PBCD results to the OBCD map in VHR images. In this paper, we present a proposed weighted Dempster-Shafer theory (wDST) fusion method to generate the OBCD by combining multiple PBCD results. The proposed wDST approach automatically calculates and assigns a certainty weight for each object of the PBCD result while considering the stability of the object. Moreover, the proposed wDST method can minimize the tendency of the number of changed objects to decrease or increase based on the ratio of changed pixels to the total pixels in the image when the PBCD result is extended to the OBCD result. First, we performed co-registration between the VHR multitemporal images to minimize the geometric dissimilarity. Then, we conducted the image segmentation of the co-registered pair of multitemporal VHR imagery. Three change intensity images were generated using change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IRMAD), and principal component analysis (PCA). These three intensity images were exploited to generate different binary PBCD maps, after which the maps were fused with the segmented image using the wDST to generate the OBCD map. Finally, the accuracy of the proposed CD technique was assessed by using a manually digitized map. Two VHR multitemporal datasets were used to test the proposed approach. Experimental results confirmed the superiority of the proposed method by comparing the existing PBCD methods and the OBCD method using the majority voting technique.

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