Semantic segmentation is one of most the important computer vision tasks for the analysis of aerial imagery in many remote sensing applications, such as resource surveys, disaster detection, and urban planning. This area of research still faces unsolved ch...
Semantic-segmentation-of-remote-sensing-image 基于深度学习关于遥感影像的语义分割 首先看一下数据集,包含原始影像与标签,实际的分辨率很大,这个只是缩略图。 影像数据是Landsat8卫星的,用五四三波段进行合成,并利用GS方法进行全色第八波段的融合。(Envi软件处理) 标签是通过矢量图层以ArcGIS软件来处理生成的。 此代码...
基本信息 \1.标题:Remote Sensing Images Semantic Segmentation with General Remote Sensing Vision Model via a Self-Supervised Contrastive Learning Method \2.作者:Haifeng Li, Yi Li, Guo Zhang, Ruoyun Liu, Haozhe Huang, Qing Zhu, Chao Tao \3.作者单位:Central South University \4.发表期刊/会议:ar...
High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region ...
In order to obtain the rough object segmentation mask, the general shape and position of the foreground object are estimated by using the high-level features in the process of layer-by-layer iteration. Then, based on the obtained rough mask, the mask is updated layer by layer using the ...
论文题目:Seeing Beyond the Patch: Scale-Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery based on Reinforcement Learning 论文链接:https://openaccess.thecvf.com/content/ICCV2023/papers/Liu_Seeing_Beyond_the_Patch_Scale-Adaptive_Semantic_Segmentation_of_High-resolution_Remote_ICCV_...
Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to the diverse landscapes and different sizes of geo-objects that RSI contains, making semantic segmentation challenging. In this paper, a convolutional network, named Adaptive Feature Fusion UNet (AFF-UNet), is ...
Semantic segmentation of remote sensing images (RSIs) is vital for numerous geospatial applications, including land-use mapping, urban planning, and environmental monitoring. Traditional neural networks for semantic segmentation primarily focus on learning in the spatial domain, which often results in subop...
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery. 展开 关键词: Deep learning feature learning hyperspectral imaging self-taught learning semantic segmentation semisupervised ...
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery * Authors: [[Libo Wang]], [[Rui Li]], [[Ce Zhang]], [[Shenghui Fang]], [[Chenxi Duan]], [[Xiaoliang Meng]], [[Peter M. Atkinson]] ...