Nonlocal similarity based DEM super resolution

Xu, Zhenlin; Wang, Xuewen; Chen, Zixuan; Xiong, Dongping; Ding, Mingyue; Hou, Wenguang

doi:10.1016/j.isprsjprs.2015.10.009

Cited by 31 publications

(26 citation statements)

References 23 publications

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“…Isola et al, 2016;Park et al, 2019), and also for producing more realistic pan-sharpened satellite images . Our DeepBedMap model builds upon these ideas and other related DEM super-resolution work (Xu et al, 2015; Chen et al, Table 1. High Resolution groundtruth datasets from ice-penetrating radar surveys (collectively labelled as y) used to train the DeepBedMap model.…”

Section: Network Conditioningmentioning

confidence: 99%

DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica

Leong¹,

Horgan²

2020

Preprint

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Abstract. To better resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that produces realistic Antarctic bed topography from multiple remote sensing data inputs. Our super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high resolution (250 m) groundtruth bed elevation grids are available. The model is then used to generate high resolution bed topography in less well surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low spatial resolution (1000 m) BEDMAP2 raster image as its prior. It takes in additional high spatial resolution datasets, such as ice surface elevation, velocity and snow accumulation to better inform the bed topography even in the absence of ice-thickness data from direct ice-penetrating radar surveys. Our DeepBedMap model is based on an adapted Enhanced Super Resolution Generative Adversarial Network architecture, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four times upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain, and by ice sheet modellers wanting to run catchment or continent-scale ice sheet model simulations. We show that DeepBedMap offers a more realistic topographic roughness profile compared to a standard bicubic interpolated BEDMAP2 and BedMachine Antarctica, and envision it to be used where a high resolution bed elevation model is required.

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Section: Network Conditioningmentioning

confidence: 99%

DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica

Leong¹,

Horgan²

2020

Preprint

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“…The DEM super resolution, a term first coined in [189] wherein the non-local algorithm focused on some learning examples which were itself derived from the original DEM by dividing the latter to overlapping patches and then categorizing as test dataset and learning dataset based on the fact whether they pertain to HR measurements or not. By calculating the weighted sums of similar patches, HR DEM were restored.…”

Section: B Literature On Cnn Based Sr Of 3d/depths and Multispectralmentioning

confidence: 99%

Multimedia super-resolution via deep learning: A survey

Hayat

2018

Digital Signal Processing

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The recent phenomenal interest in convolutional neural networks (CNNs) must have made it inevitable for the super-resolution (SR) community to explore its potential. The response has been immense and in the last three years, since the advent of the pioneering work, there appeared too many works not to warrant a comprehensive survey. This paper surveys the SR literature in the context of deep learning. We focus on the three important aspects of multimedia -namely image, video and multi-dimensions, especially depth maps. In each case, first relevant benchmarks are introduced in the form of datasets and state of the art SR methods, excluding deep learning. Next is a detailed analysis of the individual works, each including a short description of the method and a critique of the results with special reference to the benchmarking done. This is followed by minimum overall benchmarking in the form of comparison on some common dataset, while relying on the results reported in various works. Fig. 1: Backpropagation (after [7]).acceptable level of convergence whereby the optimized parameters should ideally classify each subsequent test case correctly. The birth of Convolutional neural networks (CNN) or ConvNets can be traced back to 1988 [15] 1 wherein backpropagation was employed to train a NN to classify handwritten digits. Subsequent works by LeCun evolved into what was later known as LeNet5 [17]. After that there's virtual lull till late noughties [18] when GPUs were efficient enough to culminate in the work [19]. Since then a floodgate has opened and we hear of various architectures in the form of AlexNet [20], ZFNet [21], GoogLeNet [22] DenseNet [23] etc.; for a detailed overview one can consult [18], [24].The metamorphosis from fully connected NN to locally connected NN to CNN is illustrated in Fig. 2. As can be seen, rather than being fully connected, the CNN employs convolutions leading to local connections, where each local region of the input is connected to a neuron in the output. The input to a CNN is in the form of multiple arrays, such as a color image with three 2D arrays (length × width) in accordance to RGB or YCbCr channels. The number of channels is called depth and constitutes the 3rd D; note that more than three channels are not uncommon, e.g with hyperspectral images. A CNN is made up of Layers with each layer transforming an input 3D volume to an output 3D volume [11], typically, via four distinct operations [25], viz. convolution, a non-linear activation function (ReLU), pooling or sub-sampling and classification (fully connected Layer). A simplified CNN is illustrated in Fig. 3 2 . A CNN can be described as several convolution layers with nonlinear activation functions (e.g. ReLU or sigmoid) applied to each layer. Each convolution layer applies several (may be thousands) distinct filters 3 (also called feature maps) and combines their results. These filters are automatically learnt during the training part based on the task in hand, e.g. if the task is image classification the learning concerns, a...

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“…The first data is a smooth DEM and another is the data of a mountains region which all have been used in (Xu et al, 2015). We firstly down-sampled them to 1/4, 1/9 and 1/16 pyramid images.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…DEM super resolution (Xu et al, 2015) aims to improve the resolution of a certain DEM based on the partial high resolution DEM without increasing the cost in obtaining high-accuracy equipment. It is a relatively new topic.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network Based Dem Super Resolution

Chen¹,

Wang²,

Xu³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

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Nonlocal similarity based DEM super resolution

Cited by 31 publications

References 23 publications

DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica

DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica

Multimedia super-resolution via deep learning: A survey

Convolutional Neural Network Based Dem Super Resolution

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