Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images
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TLDR
Wang et al. as discussed by the authors developed a multilevel LC contextual (MLCC) framework that can adaptively integrate the effective global context with the local context for LC classification, and the proposed MLCC has superior capability in capturing contextual features and thus outperforms the existing methods.About:
This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2022-03-01 and is currently open access. It has received 7 citations till now. The article focuses on the topics: Computer science & Computer science.read more
Citations
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Semantic segmentation model for land cover classification from satellite images in Gambella National Park, Ethiopia
TL;DR: In this paper , a deep learning-based semantic segmentation model was proposed for land cover classification in Gambella National Park (GNP) using high-resolution Sentinel-2 satellite images.
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RSCNet: An Efficient Remote Sensing Scene Classification Model Based on Lightweight Convolution Neural Networks
TL;DR: In this article , a lightweight remote sensing scene classification (RSCNet) model is proposed, which is named RSCNet, and the weights of the backbone are initialized using transfer learning, allowing the model to learn by drawing on the knowledge of ImageNet.
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Multiscale Location Attention Network for Building and Water Segmentation of Remote Sensing Image
TL;DR: Wang et al. as mentioned in this paper proposed a multiscale location attention network (MSLANet), which includes a location channel attention (LCA) unit to focus on tributary details of rivers and segmentation of building edge eaves.
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Enhancing Multiscale Representations With Transformer for Remote Sensing Image Semantic Segmentation
TL;DR: In this article , the authors proposed a novel hybrid architecture for HRRS image segmentation, termed Enhancing Multiscale Representations with Transformer (EMRT), to exploit the advantages of convolution operations and Transformer to enhance multiscale representation learning.
Journal ArticleDOI
A meta-methodology for preserving narrow objects when using spatial contextual classifiers for remote sensing data
TL;DR: In this article , a meta-methodology for improving contextual classification methods (Meta-CTX) consists in performing a separability analysis, at the pixel level, based on the class membership estimates provided by a pixel-based classifier.
References
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Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Focal Loss for Dense Object Detection
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
Journal ArticleDOI
Random forest in remote sensing: A review of applications and future directions
Mariana Belgiu,Lucian Drăguţ +1 more
TL;DR: This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting.
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Support vector machines in remote sensing: A review
TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.