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.

Urban forests globally face severe degradation due to human activities and natural disasters, making deforestation an urgent environmental challenge. Remote sensing technology and very-high-resolution (VHR) bitemporal satellite imagery enable change detection (CD) for monitoring forest changes. However, deep learning techniques for forest CD concatenate bitemporal images into a single input, limiting the extraction of informative deep features from individual raw images. Furthermore, they are developed for middle to low-resolution images focused on specific forests such as the Amazon or a single element in the urban environment. Therefore, in this study, we propose deep learning-based urban forest CD along with overall changes in the urban environment by using VHR bitemporal images. Two networks are used independently: DeepLabv3+ for generating binary forest cover masks, and a deeply supervised image fusion network (DSIFN) for the generation of a binary change mask. The results are concatenated for semantic CD focusing on forest cover changes. To carry out the experiments, full scene tests were performed using the VHR bitemporal imagery of three urban cities acquired via three different satellites. The findings reveal significant changes in forest covers alongside urban environmental changes. Based on the accuracy assessment, the networks used in the proposed study achieved the highest F1-score, kappa, IoU, and accuracy values compared with those using other techniques. This study contributes to monitoring the impacts of climate change, rapid urbanization, and natural disasters on urban environments especially urban forests, as well as relations between changes in urban environment and urban forests.

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.

Urban greenery is important for maintaining ecological balance and enhancing urban ecosystems. However, it is significantly degrading due to human activities and natural disasters, making it essential to monitor both urban greenery and the overall urban environment. Recent advancements in remote sensing and deep learning technologies have led to the development of semantic change detection (SCD) techniques, which offer more detailed analysis than binary change detection. Detecting changes in natural greenery within urban environments using general SCD techniques is challenging due to the similar spectral characteristics of natural and artificial greenery. Therefore, this study proposes a direct SCD approach focusing on urban green spaces and non-greenery-related changes. This approach distinguishes between new and degraded greenery regions and categorizes them into distinct classes alongside non-greenery changes. Key innovations include the integration of Atrous Spatial Pyramid Pooling (ASPP) and an updated spatial attention module, enhancing the network's ability to capture objects of varying shapes and sizes within urban settings. The methodology was evaluated using two open-source datasets, SECOND and WUSU, customized to emphasize urban greenery changes. Results demonstrate that our approach significantly outperforms existing SCD techniques in accurately detecting and categorizing new and degraded greenery regions alongside overall urban changes. The proposed method achieved superior performance in terms of separated kappa (Sek), reaching 17.72% on the SECOND dataset and 36.18% on the WUSU dataset. This study addresses the limitations of current methods in monitoring urban greenery, providing an efficient tool for assessing the impact of urbanization and natural disasters on urban greenery and the broader urban environment.