53,428 research outputs found
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
DETECTION OF CLOUD SHADOWS USING DEEP CNN UTILISING SPATIAL AND SPECTRAL FEATURES OF LANDSAT IMAGERY
Aim: The proposed work emphasizes here on detection of cloud shadows using Deep CNN (Convolutional Neural Networks) utilizing spatial and spectral features of Landsat imagery.
Results: In the current study deep CNN Algorithm is used for cloud and its shadow detection. We used python libraries to create a CNN. Fourier transformation is applied on that array to transform as per their requirements. Conclusion: Using the Deep CNN algorithm, we were able to combine the whole input image to get multilevel features. Deep CNN does better image processing and semantic segmentation when compared with existing fuzzy-c and f-masking.
HIGHLIGHTS:
An improved approach using Deep CNN (Convolutional Neural Network) does better image processing and semantic segmentation when compared with existing fuzzy-c and f-masking.
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