Abstract
Semantic segmentation has been widely used in the basic task of extracting information from images. Despite this progress, there are still two challenges: (1) it is difficult for a single-size receptive field to acquire sufficiently strong representational features, and (2) the traditional encoder-decoder structure directly integrates the shallow features with the deep features. However, due to the small number of network layers that shallow features pass through, the feature representation ability is weak, and noise information will be introduced to affect the segmentation performance. In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems. AMSM adopts the idea of channel and spatial attention and adaptively fuses three-channel branches by setting branching structures with different void rates, and flexibly generates weights according to the content of the image. AFM uses deep feature maps to filter shallow feature maps and obtains the weight of deep and shallow feature maps to filter noise information in shallow feature maps effectively. Based on these two symmetrical modules, we have carried out extensive experiments. On the ISPRS Vaihingen dataset, the F1-score and Overall Accuracy (OA) reached 86.79% and 88.35%, respectively.
Highlights
Semantic segmentation of remote sensing images assigns categories to each category in remote sensing images, thereby completing the pixel-level classification task
2, this isnetwork mainly composed of three parts: ResNet preprocessing encoder based on residual block; Adaptive Multi-Scale Module (AMSM); and of attention module preprocessing encoder based on residual block; AMSM; and Adaptive Fuse Module (AFM) of attention module and and upsampling block.block
We evaluated each component of the model, used ResNet-101 as our baseline, and added AFM and AMSM to enhance the consistency of the model
Summary
Semantic segmentation of remote sensing images assigns categories to each category in remote sensing images, thereby completing the pixel-level classification task. In the semantic segmentation obtains multi-scale feature information through the Hollow Space Convolution Pooling of high-resolution images, Dense Pyramid Network (DPN) [11] processes multi-sensor. The AMSM module uses an adaptive way to solve the multi-scale feature problem of remote sensing images. A novel multi-scale fusion module—ASMS (Adaptive Multi-Scale Module) module is proposed, which can adaptively fuse multi-scale features from different branches according to the size characteristics of remote sensing images and has a better segmentation effect in the data sets with complex and variable object sizes. We designed an AFM (Adaptive Fuse Module) that can filter and extract shallow information of remote sensing images. This module can combine the shallow and deep feature information effectively. AWNet achieved one of the best accuracies on the ISPRS Vaigingen data set, reaching an overall accuracy of 88.35%
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