2023 journal article
Remote Sensing Based Crop Type Classification Via Deep Transfer Learning
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 16, 4699–4712.
author keywords: Agriculture; crop classification; deep learning; remote sensing; transfer learning
TL;DR:
The findings indicate that deep neural networks pretrained on a different domain dataset cannot be used as off-the-shelf feature extractors and that using the pretrained network weights as initial weights for training on the remote sensing dataset or freezing the early layers of the Pretrained network improves the performance compared to training the network from scratch.
(via Semantic Scholar)
ieeexplore.ieee.org (publisher PDF)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: July 3, 2023
Machine learning methods using aerial imagery (satellite and unmanned-aerial-vehicles-based imagery) have been extensively used for crop classification. Traditionally, per-pixel-based, object-based, and patch-based methods have been used for classifying crops worldwide. Recently, aided by the increased availability of powerful computing architectures such as graphical processing units, deep learning-based systems have become popular in other domains such as natural images. However, building complex deep neural networks for aerial imagery from scratch is a challenging affair, owing to the limited labeled data in the remote sensing domain and the multitemporal (phenology) and geographic variability associated with agricultural data. In this article, we discuss these challenges in detail. We then discuss various transfer learning methodologies that help overcome these challenges. Finally, we evaluate whether a transfer learning strategy of using pretrained networks from a different domain helps improve remote sensing image classification performance on a benchmark dataset. Our findings indicate that deep neural networks pretrained on a different domain dataset cannot be used as off-the-shelf feature extractors. However, using the pretrained network weights as initial weights for training on the remote sensing dataset or freezing the early layers of the pretrained network improves the performance compared to training the network from scratch.
