title: Super-resolution of satellite imagery

authors: Bull, Daniel

subjects: Super-resolution

abstract: Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

text: Super-resolution of satellite imagery Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

year: 2021

title: Global relationships in tree functional traits.

authors: Maynard, D.S.

subjects: Biodiversity

abstract: Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.

text: Global relationships in tree functional traits. Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.

year: 2022-06-08

title: The Te Aroha hot springs (mainly in the nineteenth century)

authors: Hart, Philip

abstract: Unique amongst New Zealand mining districts, the hot springs sited at the base of the mountain were popular with miners, residents, and an increasing number of visitors. Highly valued by Maori for their medicinal qualities, Pakeha visited them well before gold was discovered. Acquired by the Crown as part of the purchase of the Aroha Block, contention arose over whether the springs had been gifted to the Crown and whether Maori should be charged for using them. The provision of a small but free bath to Ngati Rahiri did not satisfy them.
 After Pakeha settled, the springs were developed and the surrounding domain was landscaped. Analysis of the water by experts produced claims about its curative qualities and many miracle cures were claimed, and the water was bottled until more recent analyses traced the existence of arsenic. Men reputedly skilled in hydropathy and similar ‘sciences’ were appointed to assist those suffering from rheumatism and the like. A local board beautified the area until the domain was taken over by the Tourist Department. Many tourists from throughout New Zealand and abroad were attracted by the facilities, which included a library, but some noticed a lack of cleanliness and were annoyed by larrikins. Despite such problems, as mining faded Te Aroha profited from becoming a tourist destination and sanatorium.

text: The Te Aroha hot springs (mainly in the nineteenth century) Unique amongst New Zealand mining districts, the hot springs sited at the base of the mountain were popular with miners, residents, and an increasing number of visitors. Highly valued by Maori for their medicinal qualities, Pakeha visited them well before gold was discovered. Acquired by the Crown as part of the purchase of the Aroha Block, contention arose over whether the springs had been gifted to the Crown and whether Maori should be charged for using them. The provision of a small but free bath to Ngati Rahiri did not satisfy them.
 After Pakeha settled, the springs were developed and the surrounding domain was landscaped. Analysis of the water by experts produced claims about its curative qualities and many miracle cures were claimed, and the water was bottled until more recent analyses traced the existence of arsenic. Men reputedly skilled in hydropathy and similar ‘sciences’ were appointed to assist those suffering from rheumatism and the like. A local board beautified the area until the domain was taken over by the Tourist Department. Many tourists from throughout New Zealand and abroad were attracted by the facilities, which included a library, but some noticed a lack of cleanliness and were annoyed by larrikins. Despite such problems, as mining faded Te Aroha profited from becoming a tourist destination and sanatorium.

year: 2016