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

Sheikh, Md. Abdul Alim; Kole, Alok; Maity, Tanmoy

doi:10.1142/s0218213018500318

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…In [17], MBI and slow feature analysis were combined to carry out the building change detection. A multi-level approach for building change detection was proposed by utilizing the MBI and mutual information together [18]. MBI-based multiple building change detection results were combined with an object unit through the Dempster-Shafer theory [19].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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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Section: Introductionmentioning

confidence: 99%

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

2021

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“…A multi-index-based automatic CD method was proposed for high-resolution imagery [41]. A multi-level approach for building CD was proposed in [42] that utilizes the MBI and mutual information together.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index

et al. 2020

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Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.

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IRU-Net: An Efficient End-to-End Network for Automatic Building Extraction From Remote Sensing Images

Sheikh

Maity

Kole³

2022

IEEE Access

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Automatic extraction of buildings from High-Resolution Remote Sensing (RS) Imagery is of great practical interest for numerous applications; including urban planning, change detection, Disaster management, estimation of human population, and many other geospatial related applications. This paper proposes a novel efficient Improved ResU-Netarchitecture called IRU-Net, integrating spatial pyramid pooling module with an encoder-decoder structure, in combination with Atrous convolutions, modified residual connections, and a new skip connection between the encoder-decoder features for automatic extraction of buildings from RS images. Moreover, a new dual loss function called binary cross-entropy-dice-loss (BCEDL) is opted that make cross-entropy (CE) and dice loss (DL) and consider both local information and global information to decrease the class imbalance influence and improve the building extraction results. The proposed model is examined to demonstrate its generalization on two publicly available datasets; the Aerial Images for Roof Segmentation (AIRS) Dataset and the Massachusetts buildings dataset. The proposed IRU-Net achieved an average F-1 accuracy of 92.30% for the Massachusetts dataset and 95.65% for the AIRS dataset. When compared to other state-of-the-art deep learning-based models such as SegNet, U-Net, E-Net, ERFNet and SRI-Net, the overall accuracy improvements of our IRU-Net model is 9.0% (0.9725 vs. 0.8842), 5.2% (0.9725 vs. 0.9218), 3.0% (0.9725 vs. 0.9428), 1.4% (0.9725 vs. 0.9588) and 0.93% (0.9725 vs. 0.9635), for AIRS dataset and 11.6%, 5.9%, 3.1%, 2.7% and 1.4%, for Massachusetts dataset. These results prove the superiority of the proposed model for building extraction from high-resolution RS images.

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

Cited by 3 publications

References 26 publications

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

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

Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index

IRU-Net: An Efficient End-to-End Network for Automatic Building Extraction From Remote Sensing Images

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