A sampling position-fixing system comprising: an inputting unit that inputs spectral image data as an analyzing target; an image correlating unit that correlates the image data with a position of map data and that extracts an analyzing target area from the map data to extract a corresponding image area; a feature extracting unit that extracts feature vectors from the analyzing target area of the image data correlated with the map data by the image correlating unit; a classification processing unit that performs clustering for the extracted feature vectors on a feature space to obtain a plurality of clusters of feature vectors and that outputs a clustering result image; a space analyzing unit for performing a filtering process for the clustering result image created by the classification processing unit; means for deriving representative points from a clustering result; and an outputting unit that outputs the extracted representative points as sampling positions, wherein said means for deriving representative points includes: a center area setting unit that extracts feature vectors located in the vicinity of centers of said plurality of clusters in the feature space and maps the extracted feature vectors on an image space to derive cluster center expressing areas corresponding to the centers of the clusters; and a representative point extracting unit (110, 111) that extracts the representative points representing said plurality of clusters from a plurality of candidate points composing said cluster center expressing areas on the clustering result image, by selecting one candidate point as the representative point from the candidate points composing the cluster center expressing areas contained in areas on the clustering result image corresponding to each of the clusters on the basis of distances from other representative points, wherein when areas on the clustering result image belonging to one cluster are separately located on the clustering result image the representative point extracting unit extracts the representative point from the largest of the areas. an inputting unit that inputs spectral image data as an analyzing target; an image correlating unit that correlates the image data with a position of map data and that extracts an analyzing target area from the map data to extract a corresponding image area; a feature extracting unit that extracts feature vectors from the analyzing target area of the image data correlated with the map data by the image correlating unit; a classification processing unit that performs clustering for the extracted feature vectors on a feature space to obtain a plurality of clusters of feature vectors and that outputs a clustering result image; a space analyzing unit for performing a filtering process for the clustering result image created by the classification processing unit; means for deriving representative points from a clustering result; and an outputting unit that outputs the extracted representative points as sampling positions, a center area setting unit that extracts feature vectors located in the vicinity of centers of said plurality of clusters in the feature space and maps the extracted feature vectors on an image space to derive cluster center expressing areas corresponding to the centers of the clusters; and a representative point extracting unit (110, 111) that extracts the representative points representing said plurality of clusters from a plurality of candidate points composing said cluster center expressing areas on the clustering result image, by selecting one candidate point as the representative point from the candidate points composing the cluster center expressing areas contained in areas on the clustering result image corresponding to each of the clusters on the basis of distances from other representative points, wherein when areas on the clustering result image belonging to one cluster are separately located on the clustering result image the representative point extracting unit extracts the representative point from the largest of the areas.