04844nas|a2200469 i 4500 20260818213314.0 020208s2001 th uu m rtt 0| a1eng d .b11844504 AIT Thesis no.SR-01-09 Do Minh Phuong Satellite image texture analysis based on frequential and spatial relationships : the PAPRI method Bangkok : Asian Institute of Technology, 2001 66 leaves Thesis ; no. SR-01-09 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies Thesis (M. Eng.) - Asian Institute of Technology, 2001 The ultimate aim of any pattern recognition system is to achieve the best possil: classification accuracy for the problem to be solved (J.S. Sanchez, 1997). Classification remote sensing images is usually carried out by using approaches aimed at minimizing t overall error affecting land-cover maps (Lorenzo Bruzzone, 2000). A wide range of patte recognition techniques have appeared during the last two decades for informati extraction from remotely sensed data (Farid Melgan~ 2000). The most popular technique the maximum likelihood classification method known for its good performance a robustness. In addition, an approach to the problem of classification using artificial neu networks has been developed (S.B. Serpico and F. Roli, 1995). The implementation other image classification methods often result into fuzzy image map, where noise arise~ one of the most vital obstacles to a good landscape image. As a matter of fact, m< research conducted on satellite image classification have been focusing on this soluti However, very few applications can partly solve the problem. Recognition of objects extracted from remotely sensed imagery requires matching of object properties with prior stored knowledge. Various properties were use1 this study to form the model of a priori knowledge. The PAPRI (P Aysages defini PRiori - Landscape a priori Defined) method (Frederic Borne, 1994) proposes a good ' for classification of an image that results in a thematic layer with less noise. That met segments an image according to its textural properties as previously defined. It giv1 cutting of the image in texturally homogeneous areas, called "Landscape Units". ' product is nearer of a map than a usual classification as it proposes a synthetic fast global understanding of the image. Although there has been a lot of developmen segmentation of gray tone images in this field and other fields, like robotic vision, t has been little progress in segmentation of color or multi-band imagery. The paper pres an original way for treating a remote sensing image, with the goal to integrate it in a (Geographic Information System). The result of texture analysis is a raster imaE matrix). It is not really ready for use in a GIS. Therefore it is vectorised and a con characterizing attribute is assigned to big units to obtain the "Landscape Units" layer, rather than "Physical Environment" oriented (Frederic Borne, 1994). After ~ topological operations, it will constitute an important element of our information bas1 implement the method, an image processing software has been developed. Remote-sensing images Pattern recognition systems Borne, Frederic, Chairperson Honda, Kiyoshi, Examination Committee Andrianasolo, Haja, Examination Committee Government of Japan, Scholarship donor Asian Institute of Technology. Thesis ; no. SR-01-09 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B09436 .b11844504 mnait u 240821 0 020208 m a u 0 000:001:PDF:b1184450:002015:0:0:0:0:0:0 Abstract--AIT Thesis no.SR-01-09 n mnait mnarc mnait mnarc 22 20 40 Barcode : 30050120694376 CREATED : 2014-04-28 RECORD # : i12792597 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120694392 CREATED : 2015-12-02 RECORD # : i12839048 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050160023544 CREATED : 2016-01-04 RECORD # : i12919391 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050121017973 CREATED : 2020-10-07 RECORD # : i13281525 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 104152 104152 0 0 0 0 MAIN MAIN mnarc 2026-08-18 50.00 0 AIT Thesis no.SR-01-09 30050120694376 2026-08-18 21:33:14 3 2026-08-18 22 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT Thesis no.SR-01-09 30050120694392 2026-08-18 21:33:14 4 2026-08-18 20 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.SR-01-09 30050160023544 2026-08-18 21:33:14 1 2026-08-18 40 0 0 0 0 MAIN MAIN mnait 2026-08-18 0 AIT Thesis no.SR-01-09 30050121017973 2026-08-18 21:33:14 1 2026-08-18 20