Joint Deep Learning for land cover and land use classification

By Ce Zhang, Isabel Sargent, Xin Pan, Huapeng Li, A. Gardiner, Jonathon S. Hare, Peter M. Atkinson. Remote Sensing of Environment, 2019

458 citations recorded by the source corpus.

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications.

Subfields: Ecology, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f77d7901e8478b944c36fc353ebff15654a169ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Qiangqiang Yuan
2. Huanfeng Shen
3. Yanfei Zhong
4. Liangpei Zhang

In Media Technology

1. Liangpei Zhang
2. Huanfeng Shen
3. Yanfei Zhong
4. Qiangqiang Yuan
5. Zhuo Zheng
6. Junjue Wang
7. Ailong Ma
8. Bo Du

In Ocean Engineering

1. Yanfei Zhong
2. Huanfeng Shen
3. Liangpei Zhang

Citations in the corpus

• Relationships of groundwater quality and associated health risks with land use/land cover patterns: A case study in a loess area, Northwest China (2019, background): “Currently, with the rapid development of remote sensing technology, LULC information with higher accuracy can be extracted from remote sensing image data (Hansen and Loveland 2012; Zhang and Roy 2017; Zhang et al. 2019).”
• Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “DL has been recently introduced to land cover mapping and obtained optimal results due to its superiorities in multiscale and multilevel feature extraction (Huang et al., 2018; Scott et al., 2017b; Zhang et al., 2019).”
• Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (2020, methodology): “Semantic Segmentation in Remote Sensing Community There are a lot of applications using semantic segmentation technique in the remote sensing community, such as land use and land cover (LULC) classification [46, 23, 47], building extraction [45, 24, 43, 15], road extraction [28, 9, 2, 34, 3], vehicl”