Zobrazeno 1 - 10
of 3 980
pro vyhledávání: '"remote sensing image segmentation"'
Given a language expression, referring remote sensing image segmentation (RRSIS) aims to identify the ground objects and assign pixel-wise labels within the imagery. The one of key challenges for this task is to capture discriminative multi-modal fea
Externí odkaz:
http://arxiv.org/abs/2409.13637
Given a natural language expression and a remote sensing image, the goal of referring remote sensing image segmentation (RRSIS) is to generate a pixel-level mask of the target object identified by the referring expression. In contrast to natural scen
Externí odkaz:
http://arxiv.org/abs/2410.08613
As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within
Externí odkaz:
http://arxiv.org/abs/2410.05624
In recent years, although U-Net network has made significant progress in the field of image segmentation, it still faces performance bottlenecks in remote sensing image segmentation. In this paper, we innovatively propose to introduce SimAM and CBAM
Externí odkaz:
http://arxiv.org/abs/2408.12672
We study the potential of noisy labels y to pretrain semantic segmentation models in a multi-modal learning framework for geospatial applications. Specifically, we propose a novel Cross-modal Sample Selection method (CromSS) that utilizes the class d
Externí odkaz:
http://arxiv.org/abs/2405.01217
Diffusion models and multi-scale features are essential components in semantic segmentation tasks that deal with remote-sensing images. They contribute to improved segmentation boundaries and offer significant contextual information. U-net-like archi
Externí odkaz:
http://arxiv.org/abs/2405.20443
Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse perspectives within a scene. Recent methods leverage 3D information extracted from an Implicit Neural Field (INF), bolstering result consistency across multiple views
Externí odkaz:
http://arxiv.org/abs/2405.14171
Autor:
Yang, Yang, Zheng, Shunyi
The advancement of deep learning has driven notable progress in remote sensing semantic segmentation. Attention mechanisms, while enabling global modeling and utilizing contextual information, face challenges of high computational costs and require w
Externí odkaz:
http://arxiv.org/abs/2404.13408
While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projects such as OpenStreetMap distribute the annotation load to their commu
Externí odkaz:
http://arxiv.org/abs/2403.01641
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classi
Externí odkaz:
http://arxiv.org/abs/2402.16164