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.

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.

High-rise buildings (HRBs) as modern and visually unique land use continue to increase due to urbanization. Therefore, large-scale monitoring of HRB is very important for urban planning and environmental protection. This paper performed object-based HRB detection using high-resolution satellite image and digital map. Three study areas were acquired from KOMPSAT-3A, KOMPSAT-3, and WorldView-3, and object-based HRB detection was performed using the direction according to relief displacement by satellite image. Object-based multiresolution segmentation images were generated, focusing on HRB in each satellite image, and then combined with pixel-based building detection results obtained from MBI through majority voting to derive object-based building detection results. After that, to remove objects misdetected by HRB, the direction between HRB in the polygon layer of the digital map HRB and the HRB in the object-based building detection result was calculated. It was confirmed that the direction between the two calculated using the centroid coordinates of each building object converged with the azimuth angle of the satellite image, and results outside the error range were removed from the object-based HRB results. The HRBs in satellite images were defined as reference data, and the performance of the results obtained through the proposed method was analyzed. In addition, to evaluate the efficiency of the proposed technique, it was confirmed that the proposed method provides relatively good performance compared to the results of object-based HRB detection using shadows.

This study presents an enhanced Faster R-CNN framework that incorporates elliptical bounding boxes to significantly improve building detection in off-nadir imagery, effectively reducing severe geometric distortions caused by oblique sensor angles. Off-nadir imagery enhances architectural detail capture and reduces occlusions, but conventional bounding boxes, such as axis-aligned and rotated bounding boxes, often fail to localize buildings distorted by extreme perspectives. We propose a hybrid method integrating elliptical bounding boxes for curved structures and rotated bounding boxes for tilted buildings, achieving more precise shape approximation. In addition, our model incorporates a squeeze-and-excitation mechanism to refine feature representation, suppress background noise, and enhance object boundary alignment, leading to superior detection accuracy. Experimental results on the BONAI dataset demonstrate that our approach achieves a detection rate of 91.96%, significantly outperforming axis-aligned bounding boxes (65.75%) and rotated bounding boxes (87.13%) in detecting irregular and distorted buildings. By providing a highly robust and adaptable detection strategy, our approach establishes a new standard for accurate and shape-aware building recognition in off-nadir imagery, significantly improving the detection of distorted, rotated, and irregular structures.

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

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.