On Development of Land Cover Classification system using Remote Sensing Data in terms of Inverse Problem
Junichi Suzaki and Ryosuke Shibasaki
Center for Spatial Information Science and Institute of Industrial Science
University of Tokyo
7-22-1, Roppongi, Minato-ku, Tokyo, 106-8558 Japan
Tel: +81-3-3402-6231, Fax:+81-3408-8268
E-mail: suzaki@skl.iis.u-tokyo.ac.jp
Abstract
Considering the increase of satellite sensors in future, the data fusion process for land cover and land use monitoring will be inevitable for the effective use of those data. In this pear, the land cover classification system is regarded as a kind of inverse problem so that it can have a certain generalization which can be flexible for the fusion of data form different kinds of sensors. Then, the framework of such a classification system is presented.
1. Introduction
Recently, land cover and land classification on a huge scale, e.g. national or continental scale, has become more and more important,. Land cover and land use data are now required to have wide coverage and relatively high accuracy . especially, extraction of crop fields, paddy fields and deforested area should be paid more attention to as a result of human impacts to the environment. Remote sensing satellite can cover wide areas efficiently, and land cover and land use classification algorithm for remotely sensed images have been studied so far. In terms of use of point-based prior information , like ground truth data, classification methods are decided into two types, supervised classification and unsupervised one. One to the most popular method among supervised classification ones in Maximum Likelihood Method. It calculates similarity (likelihood ) between the target pixel and training data set, and determine which class the pixel belongs to according to the likelihood. As a traditional unsupervised classification method, clustering is offering applied because it can produce classes, in which each pixel has some similarity based on Euclidean distance. Among them, it is common that the distance in the characteristic space is calculated and is used as criteria for the determination. In terms of use of specific information, e.g. knowledge's on land cover characteristics , and expert system has been proposed as one of knowledge-based classification system.
Those classification methods provide estimations by using observed data and setting certain assumption against unknown parameter in many stages under some heuristic knowledge. Such a backwatds estimation problem, called as "inverse problem ", has been inevitable and the solution processes have been analyzed in various fields, e.g. geo-technical engineering, biophysics, and bio-medical engineering.
In our research, authors formulate the classification process as a kind of inverse problems,, then propose a framework, handling of knowledge information in section 2, an inverse problem approach for land cover classification using remote sensing data is described. In section 3, land cover classification system using TM and AVHRR images is explained as an example of the approach. Finally concussions are described in section 4.
2. Land Cover Classification system as a Process of Solving Inverse Problem.
2-1 Land Cover Classification System as a Process of Solving Inverse Problem
Figure 1 shows models for remote sensing process; sensor model, atmosphere model and object ( land cover ) model. Sensor model depends on each sensor type. Land cover model includes knowledge on land covers, e.g. spectral characteristics, seasonal changes, climate conditions and agricultural practices.
Simple linear regression model can be assumed for a model in Figure 1.
Where Y : observed data matrix
(dependent variable ) (nx1)
A: parameter matrix (nXm)
X: independent variable matrix
(mXI)
The solution approach is defined as "inverse problem " t estimate A by using Y and X, or estimate X by using Y and A. In remote sensing field, estimation of geographical characteristics from observed data, i.e remote sensing data, is a typical inverse problem. In Figure 2, the inverse problem approach for the models in Figure 1 is shown.
Figure .1 Remote Sensing Process Model
Figure.2 Inverse Problem Approach
III-posed problems are defined as the problems which cannot satisfy at least one conditions regarding the solution as below.
-
existence
- uniqueness
- continuity
- continuity
- stability
