CT-UNet: Context-Transfer-UNet for Building Segmentation in Remote Sensing Images

Abstract

With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, most networks have poor recognition ability on high resolution images, resulting in blurred boundaries in the segmented building maps. Second, the similarity between buildings and background results in intraclass inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block. Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then, to handle intra-class inconsistency, we construct Spatial Channel Attention Block. It combines context space information and selects more distinguishable features from space and channel. Finally, we propose an improved loss function to enhance the purpose of loss by adding evaluation indicator. Based on our proposed CT-UNet, we achieve 85.33% mean IoU on the Inria dataset, 91.00% mean IoU on the WHU dataset and 83.92% F1-score on the Massachusetts dataset. The results outperform our baseline (U-Net ResNet-34) by 3.76%, exceed Web-Net by 2.24% and surpass HFSA-Unet by 2.17%.

Deep Learning-Based Road Traffic Noise Annoyance Assessment

“…With the development of deep learning, a large number of researchers have tried to use deep learning on their own tasks, and many researchers have successfully applied deep learning algorithms in many of the fields such as image segmentation [ 41 ], speech enhancement [ 42 ], hearing aids [ 43 ], traffic prediction [ 44 ], and so on. In this process, many classic deep learning model architectures have been gradually created, among which the most widely used and effective model is UNet [ 45 ]: almost all of the tasks achieved good results by using UNet architecture or modifying the UNet architecture according to the requirements of the tasks [ 41 , 44 , 45 ]. The model extracts deep features of the data by adding a convolutional downsampling module [ 41 ] to the basic convolutional neural network [ 46 ] and recovers the dimensionality of the data through a convolutional upsampling [ 41 ] module.…”

“…In this process, many classic deep learning model architectures have been gradually created, among which the most widely used and effective model is UNet [ 45 ]: almost all of the tasks achieved good results by using UNet architecture or modifying the UNet architecture according to the requirements of the tasks [ 41 , 44 , 45 ]. The model extracts deep features of the data by adding a convolutional downsampling module [ 41 ] to the basic convolutional neural network [ 46 ] and recovers the dimensionality of the data through a convolutional upsampling [ 41 ] module.…”

ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image

“…Many remote sensing building extraction methods based on deep convolutional neural networks have been presented since the emergence and development of convolutional neural networks. Semantic segmentation algorithms based on the encoder-decoder structure of Unet frequently utilize the characteristics of residual connectivity and dense connectivity and have achieved numerous research achievements in the semantic segmentation of high-resolution remote sense buildings [28][29][30]. To enhance the feature representation capability of deep convolutional neural networks for remote sensing image segmentation, researchers commonly adopt the attention mechanism in the feature extraction stage to obtain more information about the target of remote sensing images and suppress the background, noise, and other interference feature of remote sensing images.…”

Comparative Study of Real-Time Semantic Segmentation Networks in Aerial Images During Flooding Events

“…Moreover, researchers used UNet as the baseline for the semantic segmentation of remote sensing images. Context-Transfer-UNet (CT-UNet) [56] applies dense boundary blocks to refine features and increase segmentation abilities. Deep residual UNet [106] integrates the robustness of residual learning and UNet structure.…”