Delineating agricultural field boundaries from TM imagery using dyadic wavelet transforms

1996 ◽  
Vol 51 (6) ◽  
pp. 268-283 ◽  
Author(s):  
C.Y. Ji
Keyword(s):  
Wavelet Transforms ◽  
Agricultural Field ◽  
Dyadic Wavelet ◽  
Field Boundaries

scholarly journals DENOISING AND ENHANCEMENT OF MAMMOGRAPHIC IMAGES UNDER THE ASSUMPTION OF HETEROSCEDASTIC ADDITIVE NOISE BY AN OPTIMAL SUBBAND THRESHOLDING

2010 ◽  
Vol 08 (05) ◽  
pp. 713-741 ◽  
Author(s):  
ARIANNA MENCATTINI ◽  
GIULIA RABOTTINO ◽  
MARCELLO SALMERI ◽  
ROBERTO LOJACONO ◽  
BERARDINO SCIUNZI
Keyword(s):  
Wavelet Transforms ◽  
Additive Noise ◽  
Subjective Evaluation ◽  
Visual Assessment ◽  
Noise Model ◽  
Zero Approximation ◽  
Low Contrast ◽  
Mammographic Images ◽  
Dyadic Wavelet ◽  
Expert Radiologist

Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation.

scholarly journals Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 722
Author(s):  
Alireza Taravat ◽  
Matthias P. Wagner ◽  
Rogerio Bonifacio ◽  
David Petit
Keyword(s):  
Network Architecture ◽  
Spatial Information ◽  
Cropping Systems ◽  
Boundary Detection ◽  
Jaccard Coefficient ◽  
Agricultural Field ◽  
Field Boundary ◽  
Convolutional Network ◽  
Fully Convolutional Networks ◽  
Field Boundaries

Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.

An Optimization to Protein Coding Regions Identification in Eukaryotes

2013 ◽  
pp. 1745-1754
Author(s):  
Muneer Ahmad ◽  
Azween Abdullah ◽  
Noor Zaman
Keyword(s):  
Continuous Improvement ◽  
Wavelet Transforms ◽  
Complex Method ◽  
Mus Musculus Domesticus ◽  
Protein Coding ◽  
Coding Regions ◽  
Dyadic Wavelet ◽  
Sequence Method ◽  
Future Expectation ◽  
Ncbi Accession Number

Significant improvement in coding regions identification was observed over many real datasets, which were obtained from the national center for bioinformatics. Quantitatively, the authors monitored a gain of 80.5% in coding identification with the Complex method, 42.5% with the Binary method, and 15% with the EIIP indicator sequence method over Mus Musculus Domesticus (House rat), NCBI Accession number: NC_006914, Length of gene: 7700 bp with number of coding regions: 4. Continuous improvement in significance with dyadic wavelet transforms will be observed as a future expectation.

scholarly journals Automated delineation of agricultural field boundaries from Sentinel-2 images using recurrent residual U-Net

2021 ◽  
Vol 105 ◽  
pp. 102557
Author(s):  
Huanxue Zhang ◽  
Mingxu Liu ◽  
Yuji Wang ◽  
Jiali Shang ◽  
Xiangliang Liu ◽  
...  
Keyword(s):  
Agricultural Field ◽  
Field Boundaries ◽  
Sentinel 2

scholarly journals Directional dyadic wavelet transforms: design and algorithms

2002 ◽  
Vol 11 (4) ◽  
pp. 363-372 ◽  
Author(s):  
P. Vandergheynst ◽  
J.-F. Gobbers
Keyword(s):  
Wavelet Transforms ◽  
Dyadic Wavelet

An Optimization to Protein Coding Regions Identification in Eukaryotes

2012 ◽  
pp. 281-290
Author(s):  
Muneer Ahmad ◽  
Azween Abdullah ◽  
Noor Zaman
Keyword(s):  
Continuous Improvement ◽  
Wavelet Transforms ◽  
Complex Method ◽  
Mus Musculus Domesticus ◽  
Protein Coding ◽  
Coding Regions ◽  
Dyadic Wavelet ◽  
Sequence Method ◽  
Future Expectation ◽  
Ncbi Accession Number

Significant improvement in coding regions identification was observed over many real datasets, which were obtained from the national center for bioinformatics. Quantitatively, the authors monitored a gain of 80.5% in coding identification with the Complex method, 42.5% with the Binary method, and 15% with the EIIP indicator sequence method over Mus Musculus Domesticus (House rat), NCBI Accession number: NC_006914, Length of gene: 7700 bp with number of coding regions: 4. Continuous improvement in significance with dyadic wavelet transforms will be observed as a future expectation.

scholarly journals Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks

2019 ◽  
Vol 12 (1) ◽  
pp. 59 ◽  
Author(s):  
Khairiya Mudrik Masoud ◽  
Claudio Persello ◽  
Valentyn A. Tolpekin
Keyword(s):  
Contextual Information ◽  
Super Resolution ◽  
Contour Detection ◽  
Agricultural Field ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Learning Technique ◽  
Field Boundaries ◽  
Sentinel 2

Boundaries of agricultural fields are important features necessary for defining the location, shape, and spatial extent of agricultural units. They are commonly used to summarize production statistics at the field level. In this study, we investigate the delineation of agricultural field boundaries (AFB) from Sentinel-2 satellite images acquired over the Flevoland province, the Netherlands, using a deep learning technique based on fully convolutional networks (FCNs). We designed a multiple dilation fully convolutional network (MD-FCN) for AFB detection from Sentinel-2 images at 10 m resolution. Furthermore, we developed a novel super-resolution semantic contour detection network (named SRC-Net) using a transposed convolutional layer in the FCN architecture to enhance the spatial resolution of the AFB output from 10 m to 5 m resolution. The SRC-Net also improves the AFB maps at 5 m resolution by exploiting the spatial-contextual information in the label space. The results of the proposed SRC-Net outperform alternative upsampling techniques and are only slightly inferior to the results of the MD-FCN for AFB detection from RapidEye images acquired at 5 m resolution.

WAVELET DIRECTIONAL HISTOGRAMS OF THE SPATIO-TEMPORAL TEMPLATES OF HUMAN GESTURES

2004 ◽  
Vol 02 (03) ◽  
pp. 283-298 ◽  
Author(s):  
ARUN SHARMA ◽  
DINESH K. KUMAR ◽  
SANJAY KUMAR ◽  
NEIL McLACHLAN
Keyword(s):  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Temporal Analysis ◽  
Sequence Matching ◽  
Directional Information ◽  
Dyadic Wavelet ◽  
Human Gestures ◽  
Spatio Temporal ◽  
High Level

This paper evaluates the efficacy of directional information of wavelet multi-resolution decomposition to enhance histogram-based classification of human gestures. The gestures are represented by spatio-temporal templates. This template collapses spatial and temporal components of motion into a static gray scale image such that no explicit sequence matching or temporal analysis is required, and it reduces the dimensionality to represent motion. These templates are modified to be invariant to translation and scale. Two-dimensional, 3-level dyadic wavelet transforms have been applied on the template resulting in one lowpass sub-image and nine highpass directional sub-images. Histograms of wavelet coefficients at different scales are used for classification purposes. The experiments demonstrate that while the statistical properties of the template provide high level of classification accuracy, the global detail activity available in highpass decompositions significantly improve the classification accuracy.

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