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Aerial Photograph and Satellite Image Classification
- https://www.cpc.unc.edu/projects/nangrong/data/spatial_data/remote_sensing/classification/index.html#:~:text=Since%20air%20photos%20lack%20spectral%20information%2C%20their%20classification,satellite%20images%20is%20applied%20to%20air%20photo%20classification.
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Texture classification in aerial photographs and satellite data
- https://www.tandfonline.com/doi/abs/10.1080/01431169208904130
- Abstract Texture features have proved to be an important tool in image segmentation and object recognition, as well as interpretation of images in a variety of applications ranging from medical imaging to remote sensing. Many methods were suggested to achieve good discrimination between different textural regions. We propose a non-supervised …
Texture classification in aerial photographs and satellite …
- https://cris.tau.ac.il/en/publications/texture-classification-in-aerial-photographs-and-satellite-data
- Many methods were suggested to achieve good discrimination between different textural regions. We propose a non-supervised classification method. The method combines a multi-resolution based texture feature with features based on first and second order statistics. These features are calculated for each pixel in the image and its neighbours.
Texture classification in aerial photographs and satellite data
- https://eurekamag.com/research/009/527/009527279.php
- Texture classification in aerial photographs and satellite data Sali, E.; Wolfson, H. International Journal of Remote Sensing 13(18): 3395-3408 1992 ISSN/ISBN: 0143-1161 DOI: 10.1080/01431169208904130
Texture classification in aerial photographs and satellite …
- https://www.semanticscholar.org/paper/Texture-classification-in-aerial-photographs-and-Sali-Wolfson/78364de0149637e591b674b29f9aff631c169b8c
- Abstract Texture features have proved to be an important tool in image segmentation and object recognition, as well as interpretation of images in a variety of applications ranging from medical imaging to remote sensing. Many methods were suggested to achieve good discrimination between different textural regions. We propose a non-supervised classification method. The …
Texture classification in aerial photographs and satellite …
- https://ui.adsabs.harvard.edu/abs/1992IJRS...13.3395S/abstract
- Texture classification in aerial photographs and satellite data Sali, E.; Wolfson, H. Abstract. Publication: International Journal of Remote Sensing. Pub Date: December 1992 DOI: 10.1080/01431169208904130 Bibcode: 1992IJRS...13.3395S full text sources ...
Aerial Photograph and Satellite Image Classification
- https://www.cpc.unc.edu/projects/nangrong/data/spatial_data/remote_sensing/classification/index.html
- Another major difference between air photos and satellite images in the approach to classification results from the nature of the data. Since air photos lack spectral information, their classification is based on the texture, pattern, lightness/darkness, and context of the features on the photos. As such, none of the higher level image processing that is utilized in …
Textural Attributes Classification of High Resolution …
- http://article.sapub.org/10.5923.j.ajgis.20180705.01.html
- Statistical methods are extensively used in texture classification. Properties such as gray-level co-occurrence, contrast, entropy, and homogeneity are computed from image gray levels to facilitate classification. Many texture measures have been developed [5] and have been used for image classifications [6].
Soil texture classification with artificial neural
- https://www.sciencedirect.com/science/article/pii/S0168169906000834
- The pixel windows in the aerial photographs were extended two, three, four, and five additional pixels in each direction, resulting in four aerial data subsets with 25, 49, 81, and 121 single-band pixels related to each sample point . The visible intensity values of these pixels were normalized to from 0 to 1 by dividing each pixel value by 255, which is the maximum …
Textural Features for Image Classification
- https://home.cis.rit.edu/~cnspci/references/dip/segmentation/haralick1973.pdf
- be a photomicrograph, an aerial photograph, or a satellite image. This paper describes someeasily computabletextural features based ongray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photo-micrographs of five kinds ofsandstones, 1:20000 panchromatic aerial
Principals and Elements of Image Interpretation
- http://www.edc.uri.edu/nrs/classes/NRS409509/RS/Lectures/409-509PhotoInterpretation_2011.pdf
- Cover Classification Level Representative Format for Image Interpretation I Low to moderate resolution satellite data (e.g., Landsat MSS) II Small-scale aerial photographs; moderate resolution satellite data (e.g., Landsat TM) III Medium-scale aerial photographs; high resolution satellite data (e.g., IKONOS) IV Large-scale aerial photographs
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