Semantic segmentation sample augmentation based on simulated scene generation—case study on dock extraction from high spatial resolution imagery
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2021.1907866
Abstract: ABSTRACT Deep learning-based semantic segmentation methods, such as fully convolutional networks (FCNs), are state-of-the-art techniques for object extraction from high spatial resolution images. However, collecting massive scene-formed training samples typically required in FCNs is time-consuming… read more here.
Keywords: sample; extraction; sample augmentation; dock extraction ... See more keywords
Iterative Spatial-Spectral Training Sample Augmentation for Effective Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3131373
Abstract: Factors such as insufficient training samples, high-dimensional data features, and unbalanced data classes can degrade the accuracy of hyperspectral classification. To this end, this letter proposes an iterative training sample augmentation (ITSA) algorithm and a… read more here.
Keywords: spatial spectral; classification; sample augmentation; training sample ... See more keywords