A comparative study was conducted for estimation of the sea surface temperature of the pixel suffered from partial cloud cover within a pixel. Three methods for estimation of partial cloud cover within a pixel, based on well known least square method and maximum likelihood method, were compared. It was found that around 9% of RMS error can be achieved. Also it was found that estimation accuracy highly depends on variance of representative vectors for cloud and the ocean, or observed noise. The experimental results with simulated data show RMS error of Generalized Inverse Matrix Method is highly dependent to the noise followed by Maximum Likelihood Method and Least Square Method. The results also show the best estimation accuracy can be achieved for Maximum Likelihood Method followed by Least Square Method and Generalized Inverse Matrix Method.
Lineament and geological information extraction from Remote Sensing data is important for the analysis of natural resources exploration. It is understand that lineaments are mostly reflecting the fractures of subsurface. These fractures sometimes work for the migration and stratigraphic trap of petroleum from deeper portion of source rocks. From this point of view, In this paper, the quantitative evaluation using extracted lineaments pattern from Landsat TM has been studied. Two Laplacian types of edge enhancement processing were applied to compare the effect of enhancement. Lineaments were extracted by two interpretors from 9 processed images, and those extracted lineaments were analyzed statistically. As the results, those lineamets were remarkably appeared defferently depending upon the features of each studied areas and edge enhancement parameters. Maximum cumlative length of extracted lineaments were nominaly appeared from simply enhanced images. Medium length of lineaments were also appeared from enhanced images.
A categorization method for cluster is necessary when an unsupervised learning is used for remote sensing image data classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic categorization methods were proposed and evaluated. They are 1) maximum number method, which assigns the target cluster to the category that occupies the largest area of that cluster; 2) maximum occupation rate method, which assigns the target cluster to the category which shows the maximum occupation rate within the catetory in that cluster; 3) minimum distance method, which assigns the target cluster to the category having minimum distance with that cluster; 4) element ratio method, which assigns the target region to the category which has the most similar element ratio with that region, it was certified that the result by the minimum distance method was almost same as the result made by a human operator.