title: Deep supervised learning for land cover classification from remotely sensed imagery by convolutional neural networks
Creators: name: Kattan, Eiman
Publisher: name: University of Reading
Year: 2022

description: In LULC (land-use land-cover) studies, a wide range of sensors have visualised a vast 
number of very high-resolution remote sensing (RS) aerial scenes, gathering images
with very fine spatial details that are both spectrally and spatially complicated. As such, 
a fundamental challenge is that of automating this classification to achieve maximum 
advantage. Recently, there has been a wave of excitement in deep learning regarding 
modelling high-level abstractions through hierarchical feature representations, an 
approach that demonstrates great potential in automating the interpretation of LULC 
patterns without human-designed features or rules.
In this research, a set of novel deep learning classification methods based on deep 
convolutional neural networks (CNN) are investigated and adapted. The modelling 
consists of two phases, one-scene classification and object-based segmentation, both 
applied to remotely sensed images for land cover classification. For one-scene 
classification, some generated rules are deduced to tackle the limitation of the limited
number of RS images. AlexNet was utilised in conjunction with two exploration 
strategies, fine-tuned and fully trained, thus revealing an effective result. The filter size
batches, and epochs and input sizes can be scientifically tuned for better performance. 
On the other hand, comparing large epochs to batch size reveals a positive result for a 
smaller number of selected batches. In the second part of this thesis, the most recent 
Fully Convolutional Neural (FCN) network architectures for object-based segmentation 
are considered by utilising multiple state-of-the-art architectures which address the 
limitations of standard CNN classifiers. To improve the effectiveness of such efficient 
state-of-the-art architectures, an extensive adaption experiment was carried out to 
evaluate the performance of FCDenseNet, PSPNet and MobileUNet, which proved to 
be extremely powerful for land cover segmentation, specifically for small objects, by 
investigating the effect of the number of layers of each Densenblock, increased max 
pooling layers and skipped connections, respectively. In addition, comprehensive 
comparison experiments using the most recent state-of-the-art semantic segmentation 
architectures (FC-DenseNet, GCNet, PSPNet, DeepLab, Encoder-Decoder, AdapNet 
and RefineNet) were implemented, revealing the superior performance of FC-DenseNet 
on land cover segmentation, specifically small-object segmentation, due to the 
successive iterative concatenations of dense blocks and skip connections that capture 
information from the loss. To further improve the localisation of previously predicted 
maps, an analytical investigation was applied to measure the effectiveness of a
localisation-improving method (FCCRF) in which a Gaussian kernel is used to define 
the pairwise potentials, which qualitatively improves large object boundaries. Thus, a 
visual labelling improvement is noticed, where excessive smoothing might harm small, 
isolated regions. The main contribution of the deep learning techniques is achieved by 
the development of a customised architecture that is built upon the best-performing 
deep segmentation architecture (FC-DenseNet), resulting in an improvement of 0.96% 
in overall average testing accuracy and improvements of 2.7% and 3% in average 
accuracy scores for cars and trees, respectively, compared to the baseline model where 
data augmentation achieved an improvement for the low vegetation class of 6.3% in the 
F1 score and 12% in the average accuracy score. This research makes a significant 
contribution to LULC classification through deep-learning-based innovations and has 
potential utility in a wide range of geospatial applications.