Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

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Title:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: A Method for Proportion Estimation of Mixed Pixel (MIXEL) by Means of Inversion Problem Solving. Kohei Arai and Yasunori Terayama

Document type:: Multivolume work

Structure type:: Chapter

Haidas, «»text pe r . seen for all led w ith the Ending pi x . loca l condì- ma ge resolu- lth m was in § er than for 'therworkis »crease ela,. “tihcation. n. Ed. P. »servation ( istics. 1EF A Method for Proportion Estimation of Mixed Pixel(MIXED by Means of Inversion Problem Solving Kohei Arai and Yasunori Terayama Information Science Department Science and Engineering Faculty Saga University 1 Honjo, Saga-city, Saga 840 Japan ABSTRACT A basic idea of a contextual classification by means of a method for proportion estimation of Mixed Pixel(MIXED featuring Inversion Problem Solving technique together with a concept of a Image Classification Artificial Intel 1igence(ICAI) with a hierarchical multi-information integration are proposed. An example with Landsat-5 TM data shows that estimated proportions are useful as a contextual information when the pixel of interest is MIXEL and is classified to an undesirable class in the spectral and spatial feature space. Introduction 2. Basic concept of 1CA1 system with a hierarchical multi-information integration As well known, not only spectral and spatial information derived from remote sensing satellite imagery data but also relational information such as inclusion, connection, etc. can be used in image classification method. Fig.1 shows a hierarchical structure of multi-information to be used for image classification. In human perception process, three layered multi-information are referred in almost same time. The proposed 1CAI system should include such information with flexible accessibility and with traveling among the layers back and forth while backward and forward reasoning should be capable. Discontinuities of roads are sometime observed in remote sensing satellite data. It depends on the relationship between Instantaneous Field of View(IFOV) and road width and/or the angle between road and scanning directions. The pixels in the discontinued portion of roads tends to be classified as an improper class in such case because of their properties in spectral and spatial feature spaces. Contextual information is useful in such situation together with information on mixing ratio or proportion of each class in the pixel of interest. Not so many papers dealt with proportion estimation were published(Ref. 1 - 3). Namely the method based on the feature mixing concept, the method with a generalized inversion matrix, the method based on the second order programming, the least square mean of method and linear regressive analysis with surrounding 8 neighbor pixels. The proposed method is based on Inversion Problem Solving method in a straight forward manner. The proposed method is one of the elements of the Image Classification Artificial Intel 1igence(ICAI) with a hierarchical multi information integration proposed herewith. First, a basic concept on the ICA1 is described followed by proposition of the proportion estimation method using Inversion Problem Solving. Then an example with Landsat-5 TM data of Saga, Japan is shown. For instance homogeneous segments may be classified using only spectral information. On the other hand, spatial information should be considered for heterogeneous segments. It can be divided into two categories, microscopic and macroscopic properties. The former is based on relationships between the pixel of interest and its surrounding pixels while the latter is a spatial structure in the image of interest such as road network, etc. Contextual classification and spatial treatments are useful for such pixels. Beside these relational information can be also used. Contradictions are sometime observed between classified results obtained through spectral and spatial classifications. For instance, the aforementioned discontinued segments between continuous road segments are classified as the classes other than road class because of their spectral signature. Sometime roads are covered with crown of trees. Beside this, narrow width roads compared to IFOV tend to be discontinued in the image depending upon the angle between the road and scanning direction, ratio of road width to IFOV, etc. In such situation, contextual information should be taken into account together with relational information. The proposed ICAI system allows us to refer such information including in the hierarchical layered structure.

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