Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

2013 ◽  
Author(s):  
Corey F. Bangs ◽  
Fred A. Kruse ◽  
Chris R. Olsen
Keyword(s):  
Spectral Dependence ◽  
Texture Features ◽  
Hyperspectral Data ◽  
Target Classification

scholarly journals Texture Based Hyperspectral Image Classification

2014 ◽  
Vol XL-8 ◽  
pp. 793-798 ◽  
Author(s):  
B. Kumar ◽  
O. Dikshit
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Research Work ◽  
Texture Features ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Additional Information ◽  
Geometric Moments ◽  
Classification Framework

This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can derive shape characteristics, elongation, and orientation along its axis. In this investigation second order geometric moments within small window around each pixel are computed which are further used to compute texture features. The textural and spectral features of the image are combined to form a joint feature vector that is used for classification. The experiments are performed on different types of hyperspectral images using multi-class one-vs-one support vector machine (SVM) classifier to evaluate the robustness of the proposed methodology. The results demonstrate that integration of texture features produced statistically significantly better results than spectral classification.

scholarly journals Algorithm for eliminating gas absorption effects on hyperspectral remote sensing data

2018 ◽  
Vol 42 (2) ◽  
pp. 328-337 ◽  
Author(s):  
O. V. Nikolaeva
Keyword(s):  
Remote Sensing ◽  
Correction Factor ◽  
Efficient Algorithm ◽  
Spectral Dependence ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Gas Concentration ◽  
Sensing Data ◽  
Ground System

An efficient algorithm for the elimination of gas absorption effects on the reflectance of sunlight in an atmosphere-ground system is proposed. The algorithm does not require aerosol, surface and gas concentration information. The corrected reflectance is obtained via the correction factor, which is found via analysis of the spectral dependence of reflectance. The algorithm is applicable only to hyperspectral data. Results of testing on model problems are presented.

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

Distinction of Wet Road Surface Condition at Night-time using Texture Features

2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface ◽  
Night Time

AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA

2010 ◽  
Vol 69 (6) ◽  
pp. 537-563 ◽  
Author(s):  
N. N. Ponomarenko ◽  
M. S. Zriakhov ◽  
A. Kaarna
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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