Multi-Feature Enhanced Building Change Detection Based on Semantic Information Guidance

Abstract

Building change detection has always been an important research focus in production and urbanization. In recent years, deep learning methods have demonstrated a powerful ability in the field of detecting remote sensing changes. However, due to the heterogeneity of remote sensing and the characteristics of buildings, the current methods do not present an effective means to perceive building changes or the ability to fuse multi-temporal remote sensing features, which leads to fragmented and incomplete results. In this article, we propose a multi-branched network structure to fuse the semantic information of the building changes at different levels. In this model, two accessory branches were used to guide the buildings’ semantic information under different time sequences, and the main branches can merge the change information. In addition, we also designed a feature enhancement layer to further strengthen the integration of the main and accessory branch information. For ablation experiments, we designed experiments on the above optimization process. For MDEFNET, we designed experiments which compare with typical deep learning model and recent deep learning change detection methods. Experimentation with the WHU Building Change Detection Dataset showed that the method in this paper obtained accuracies of 0.8526, 0.9418, and 0.9204 in Intersection over Union (IoU), Recall, and F1 Score, respectively, which could assess building change areas with complete boundaries and accurate results.

Building change detection using the parallel spatial-channel attention block and edge-guided deep network

“…The embedding space is trained so that the similarly embedded vectors come closer and the dissimilarity embedding vectors move away from each other (Zhan et al, 2017). This method is used more effectively to evaluate image pair details than the classification-based method (Xue et al, 2021). The embedding space can be formed with Siamese networks (Zhan et al, 2017) that include two networks with shared weights or two separate networks with independent weights .…”

Overview of the Special Issue on Applications of Remote Sensing Imagery for Urban Areas

“…This Special Issue (SI) aims to invite recent advances in the applications of RS imagery for urban areas, and 17 papers in total were selected and published. Among them, 12 papers emphasize the novel urban application algorithms based on RS imageries, such as urban attribute mapping, building extraction, classification, change detection, and so on [1][2][3][4][5][6][7][8][9][10][11][12], and 5 papers directly employed RS imageries to analyze the environmental variations and urban expansion in typical cities, such as urban heat island, air pollution, lightning, and so on [13][14][15][16][17].…”

“…RS imageries provide new opportunities to extract the urban building information and detect its changes, and thus there are four papers focused on this issue [1][2][3][4]. Cao et al [1] proposed a stacking ensemble deep learning model (SENet) to obtain fine-scale spatial and spectral building information, based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models.…”

“…The model was assessed by a building dataset in Hebei Province, China, and the results indicate that its accuracy is significantly improved compared to all three models. Xue et al [2] proposed a multi-branched network structure to fuse the semantic information of the building changes at different levels. Experimentation with the WHU Building Change Detection Dataset showed that the proposed method obtained accuracies of 0.8526, 0.9418, and 0.9204 in IoU, Recall, and F1 Score, respectively, which could assess building change areas with complete boundaries and accurate results.…”

“…In summary, this SI provides an enhanced understanding of applications of RS imagery for urban areas. New methodologies are presented for extracting the building information, modeling urban attributes and population, and detecting the urban flooding using remote sensing imageries [1][2][3][4]10,12]. Novel models for RS classification, target and change detection are also included, which can significantly support further urban applications [5][6][7][8][9].…”