PRELIMINARY TEXTURAL ANALYSIS OF THE ANTLERS SANDSTONE, TAYLOR COUNTY, TEXAS

2020 ◽  
Author(s):  
Julie Michelle Deen ◽  
◽  
Chase Dillion Mann ◽  
Arturo De Los Santos Garcia ◽  
Steven J. Rosscoe
Keyword(s):  
Textural Analysis

An Automated Classification System for the Differentiation of Obstructive Lung Diseases based on the Textural Analysis of HRCT images

2007 ◽  
Vol 57 (1) ◽  
pp. 21
Author(s):  
Seong Hoon Park ◽  
Joon Beom Seo ◽  
Namkug Kim ◽  
June Goo Lee ◽  
Young Kyung Lee ◽  
...  
Keyword(s):  
Classification System ◽  
Lung Diseases ◽  
Textural Analysis ◽  
Automated Classification ◽  
Obstructive Lung Diseases

TEXTURAL ANALYSIS OF A FLOW DIFFERENTIATED, PRE-PENOKEAN MAFIC DIKE IN THE UPPER PENINSULA, MICHIGAN

2017 ◽  
Author(s):  
Emma Jean Peterson ◽  
◽  
Ryan Currier
Keyword(s):  
Textural Analysis ◽  
Upper Peninsula ◽  
Mafic Dike

TEXTURAL ANALYSIS OF AMPHIBOLITES AND SCHIST FROM THE CENTRAL VIRGINIA SEISMIC ZONE: CONSTRAINING THE PALEOZOIC TECTONIC FRAMEWORK IN A SEISMICALLY ACTIVE REGION

2017 ◽  
Author(s):  
Mercer Parker ◽  
◽  
Mark W. Carter ◽  
Ryan J. McAleer ◽  
William C. Burton ◽  
...  
Keyword(s):  
Active Region ◽  
Textural Analysis ◽  
Seismic Zone ◽  
Tectonic Framework

BREADCRUST BUBBLES: ESTIMATING EXPLOSIVE ERUPTION EXIT VELOCITY THROUGH TEXTURAL ANALYSIS

2019 ◽  
Author(s):  
Benjamin J. Andrews ◽  
◽  
Steve Quane
Keyword(s):  
Explosive Eruption ◽  
Textural Analysis ◽  
Exit Velocity

scholarly journals Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

2013 ◽  
Vol 213 (1) ◽  
pp. 84-98 ◽  
Author(s):  
Umasankar Kandaswamy ◽  
Ziv Rotman ◽  
Dana Watt ◽  
Ian Schillebeeckx ◽  
Valeria Cavalli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Textural Analysis ◽  
Machine Learning Algorithms

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

Hybrid Structural and Textural Analysis for Efficient Image Compression

2021 ◽  
Author(s):  
B. Vidhya ◽  
R. Vidhyapriya
Keyword(s):  
Image Compression ◽  
Textural Analysis

The textural analysis of cooked potato. 1. Physical principles of the separate measurement of softness and dryness

1992 ◽  
Vol 35 (1) ◽  
pp. 83-91 ◽  
Author(s):  
M. C. Jarvis ◽  
H. J. Duncan
Keyword(s):  
Textural Analysis ◽  
Separate Measurement ◽  
Physical Principles
Sign in / Sign up

Export Citation Format

Share Document