Explore the vast range of titles available.

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments.

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.

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion.

Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D change detection algorithms are based on the independent segmentation of two-temporal images and the feature fusion of spectral change and height change. These methods do not consider 3D change information and spatial context information simultaneously. In this paper, we propose a novel building change detection algorithm based on 3D Co-segmentation, which makes full use of the 3D change information contained in the stereoscope data. An energy function containing spectral change information, height change information, and spatial context information is constructed. Image change feature is extracted using morphological building index (MBI), and height change feature is obtained by robust normalized digital surface models (nDSM) difference. 3D Co-segmentation divides the two-temporal images into the changed foreground and unchanged background through the graph-cut-based energy minimization method. The object-to-object detection results are obtained through overlay analysis, and the quantitative height change values are calculated according to this correspondence. The superiority of the proposed algorithm is that it can obtain the changes of buildings in planar and vertical simultaneously. The performance of the algorithm is evaluated in detail using six groups of satellite datasets. The experimental results prove the effectiveness of the proposed building change detection algorithm.

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.

Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.

In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value.

The problem of high quality training samples and high-dimensional data is encountered in high-resolution image change detection. To address these problems, a novel automatic change detection algorithm in bitemporal multispectral images of the same scene using multiindex information is presented. The conspicuous advantages of the proposed algorithm are: (i) the complicated urban scenes are represented by a set of low-level semantic information index (e.g., textural and structural features), the information indices can directly indicate the primitive urban classes and (ii) change detection is carried out automatically using unsupervised approach. The multiindex information map contains vegetation, water and building extracted using enhanced vegetation index, normalized difference water index and developed efficient morphological building index respectively. The proposed algorithm is validated on the multitemporal Landsat ETM+ images over Coimbatore, Tamilnadu, India where auspicious results were achieved by the proposed method. Moreover, the traditional method based on the pixel based change detection has also been implemented for the purpose of comparison to further validate the advantages of the proposed model.

A new morphological attribute building index (MABI) and shadow index (MASI) are proposed here for automatically extracting building features from very high-resolution (VHR) remote sensing satellite images. By investigating the associated attributes in morphological attribute filters (AFs), the proposed method establishes a relationship between AFs and the characteristics of buildings/shadows in VHR images (e.g., high local contrast, internal homogeneity, shape, and size). In the pre-processing step of the proposed work, attribute filtering was conducted on the original VHR spectral reflectance data to obtain the input, which has a high homogeneity, and to suppress elongated objects (potential non-buildings). Then, the MABI and MASI were calculated by taking the obtained input as a base image. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. To better detect buildings from the MABI feature image, an object-oriented analysis and building-shadow concurrence relationships were utilized to further filter out non-building land covers, such as roads and bare ground, that are confused for buildings. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. In view of both the visual inspection and quantitative assessment, the results of the proposed work are superior to recent automatic building index and supervised binary classification approach results.