.png){kind=icon}
返回
Remote sensing image instance segmentation network with transformer and multi-scale feature representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2023.121007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The goal of remote sensing image (RSI) instance segmentation is to perform instance-level semantic parsing of its contents. Aside from classifying and locating regions of interest (RoI), it also requires assigning finer pixel -wise annotations to objects. However, RSI often suffers from cluttered backgrounds, variable object scales, and complex object edge contours, making the instance segmentation task more challenging. In this work, we analytically customize an instance segmentation model that is more suitable for RSI. Specifically, we propose three novel modules for a region-based instance segmentation framework, namely Channel-Spatial Attention Module (CSA), Multi-Scale Aware Module (MSA), and Semantic Relation Learning Module (SRL). Among them, feature calibration performed by CSA can alleviate the semantic gap between low-level features and high-level semantics in both channel and spatial dimensions. Inheriting the capabilities of both the convolutional neural network (CNN) and the Transformer, SRL can help the network integrate both neighborhood features and long-range dependencies for instance semantic prediction. The MSA module designs a cascaded residual structure with different receptive fields to model the scale variation of objects in RSI. Experimental results on challenging ISAID, NWPU VHR-10, SSDD, BITTC and HRSID datasets demonstrate the superiority of our method, achieving mask APs of 40.2%, 68.2%, 68.4%, 50.4% and 55.8% respectively. Code and pretrained models are available at https://github.com/Sherlock1018/RSIISN.
Keyword:
Remote sensing image
Instance segmentation
Region-based
Long-range dependencies
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Conductivity Enhancement in Thin Silicon-on-Insulator Layer Embedding Artificial Dislocation Network
.png){kind=icon}
PREAI
分享收藏Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏Spatiotemporal differentiation and geographic detection mechanism of ecological security in Chongqing, China重庆市生态安全时空分异及地理检测机制
OAAI
OAAI分享收藏A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method集成兴趣点空间上下文和图卷积神经网络方法的城市土地利用分类框架
PREAI
分享收藏分享收藏Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform
OAAI
OAAI分享收藏
查看更多内容