Asset Details
Do you wish to reserve the book?
A \MS Convolution-HC Texture\ Filtering proposed Method to Spatially Enhance Supervised - Unsupervised Land Cover Classification Analysis
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A \MS Convolution-HC Texture\ Filtering proposed Method to Spatially Enhance Supervised - Unsupervised Land Cover Classification Analysis
Do you wish to request the book?
A \MS Convolution-HC Texture\ Filtering proposed Method to Spatially Enhance Supervised - Unsupervised Land Cover Classification Analysis
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A \MS Convolution-HC Texture\ Filtering proposed Method to Spatially Enhance Supervised - Unsupervised Land Cover Classification Analysis
2023
Overview
Image Classification has formed an important part of the fields of remote sensing, image analysis, and pattern recognition. This paper is an attempt to introduce a new method of classification enhancement by merging both median spatial convolution filtering (MSCF) and grey level homogeneity co-occurrence texture filter (HCTF). The size of the neighborhood convolution mask or Kernel (n) is constrained to be 3×3 with nine coefficients (Ci). In this respect, two different spatially, spectrally, and temporally satellite data imagery are used. The American Landsat Enhanced Thematic Mapper Plus (ETM+) is used in comparison with the French SPOT data imagery. Two types of SPOT satellite images are used. The first is in the multispectral mode (Xs), which is SPOT-5 of High-Resolution Geometric (HRG) data. While the second is in the panchromatic (PAN) mode, which is SPOT-2 PAN. The proposed method involves applying the technique through examining both supervised and unsupervised classification by using the minimum distance classifier method and the ISODATA algorithm method, respectively. Furthermore, this study experiences the usage of the proposed method in different spatially resampled data imagery and ends with a post classification accuracy assessment that significantly offers satisfying overall accuracies and kappa coefficient results. Additionally, it reduces the error percentage up to approximately 7% difference when compared to the conventional method. Al- Madinah Al-Munawarah in Saudi Arabia was chosen as a case study area to conduct the analysis because of its diversity and variety in terms of land cover and land use classes.
Publisher
جامعة القاهرة - فرع الخرطوم - كلية الآداب
Subject
