MAD-UNet: A Multi-Region UAV Remote Sensing Network for Rural Building Extraction

Abstract

For the development of an idyllic rural landscape, an accurate survey of rural buildings is essential. The extraction of rural structures from unmanned aerial vehicle (UAV) remote sensing imagery is prone to errors such as misclassifications, omissions, and subpar edge detailing. This study introduces a multi-scale fusion and detail enhancement network for rural building extraction, termed the Multi-Attention-Detail U-shaped Network (MAD-UNet). Initially, an atrous convolutional pyramid pooling module is integrated between the encoder and decoder to enhance the main network’s ability to identify buildings of varying sizes, thereby reducing omissions. Additionally, a Multi-scale Feature Fusion Module (MFFM) is constructed within the decoder, utilizing superficial detail features to refine the layered detail information, which improves the extraction of small-sized structures and their edges. A coordination attention mechanism and deep supervision modules are simultaneously incorporated to minimize misclassifications. MAD-UNet has been tested on a private UAV building dataset and the publicly available Wuhan University (WHU) Building Dataset and benchmarked against models such as U-Net, PSPNet, DeepLabV3+, HRNet, ISANet, and AGSCNet, achieving Intersection over Union (IoU) scores of 77.43% and 91.02%, respectively. The results demonstrate its effectiveness in extracting rural buildings from UAV remote sensing images across different regions.

An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images

“…To verify the effectiveness of the proposed AFSF network, we conducted experiments on the new roof segmentation dataset and two common remote sensing semantic image segmentation datasets, i.e., the Inria Aerial Image Labeling (IAIL) [19] and WHU [20] datasets, and compare the results with several state-of-the-art methods, including Deeplab V3 [55], UNet [37], U2Net [44], HED [56], RCF [57], BASNet [58], MA [42], AS-UNet++ [59], and MAD-UNet [60].…”

“…For example, on the IAIL dataset, the proposed AFSF network outperforms the second-best model, AS-UNet++ [59], by 0.7%, 0.7%, and 1.1% in terms of the precision, recall, and IoU, respectively; on the WHU dataset, the proposed AFSF network outperforms the second-best model, AS-UNet++ [59], by 0.6%, 0.5%, and 0.8% in terms of the precision, recall, and IoU, respectively. [58] 95.9 92.9 89.3 HED [56] 94.2 90.4 85.6 RCF [57] 95.0 90.7 86.6 MA [42] 96.0 93.2 89.7 AS-UNet++ [59] 96.1 93.4 90.1 MAD-UNet [60] 95.9 93.0 89.6 AFSF 96.7 93.9 90.9 Figure 6. Some examples of prediction masks generated by the proposed AFSF and compared methods.…”

RCEAU-Net: Cascade Multi-Scale Convolution and Attention-Mechanism-Based Network for Laser Beam Target Image Segmentation with Complex Background in Coal Mine

“…Constrained by the traditional algorithm design difficulties, whereby it is difficult to ensure real-time target segmentation in the complex background of poor results and other issues, researchers have begun to choose to use deep learning-based image semantic segmentation methods to build segmentation models to complete the target segmentation task. Currently, the design of segmentation models based on the encoder–decoder structure of a full convolutional neural network FCN [ 9 ] is quite extensive, among which, due to the relatively simple structure of the U-Net [ 10 ] model and its outstanding segmentation performance, it and its variants have now achieved remarkable results in the semantic segmentation tasks of images such as medicine [ 11 ], traffic [ 12 ], agriculture [ 13 ], aerial photography [ 14 ], remote sensing [ 15 ], and so on. O. Oktay et al [ 16 ] proposed a novel Attention Gate (AG) model for the medical image domain, which can automatically learn to focus on target structures of different shapes and sizes, and integrated it into the U-Net network architecture to build the Attention U-Net network, which reduces the computational overheads of the original U-Net model, and improves the model’s sensitivity and computational accuracy.…”