计算机科学
分割
人工智能
图像分辨率
计算机视觉
图像分割
分辨率(逻辑)
块(置换群论)
交叉口(航空)
模式识别(心理学)
几何学
数学
工程类
航空航天工程
作者
Qian Zhang,Guang Yang,Guixu Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-12
被引量:31
标识
DOI:10.1109/tgrs.2021.3099300
摘要
In the past few years, multitask learning (MTL) has been widely used in a single model to solve the problems of multiple businesses. MTL enables each task to achieve high performance and greatly reduces computational resource overhead. In this work, we designed a collaborative network that simultaneously solves the super-resolution semantic segmentation and super-resolution image reconstruction. This algorithm can obtain high-resolution semantic segmentation and super-resolution reconstruction results by taking relatively low-resolution images as input when high-resolution data are inconvenient or computing resources are limited. The framework consists of three parts: the semantic segmentation branch (SSB), the super-resolution branch (SRB), and the structural affinity block (SAB). Specifically, the SSB, SRB, and SAB are responsible for completing super-resolution semantic segmentation, image super-resolution reconstruction, and associated features, respectively. Our proposed method is simple and efficient, and it can replace the different branches with most of the state-of-the-art models. The International Society for Photogrammetry and Remote Sensing (ISPRS) segmentation benchmarks were used to evaluate our models. In particular, super-resolution semantic segmentation on the Potsdam dataset reduced Intersection over Union (IoU) by only 1.8% when the resolution of the input image was reduced by a factor of two. The experimental results showed that our framework can obtain more accurate semantic segmentation and super-resolution reconstruction results than the single model.
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