Many image-processing techniques are based on texture features or gradation features of the image. However, Landsat images are complex; they also include physical features of reflection radiation and heat radiation from land cover. In this paper, we describe a method of constructing a super-resolution image of Band 6 of the Landsat TM sensor, oriented to analysis of an agricultural area, by combining information (texture features, gradation features, physical features) from other bands. In this method, a knowledge-based hierarchical classifier is first used to identify land cover in each pixel and then the least-squares approach is applied to estimate the mean temperature of each type of land cover. By reassigning the mean temperature to each pixel, a finer spatial resolution is obtained in Band 6. Computational results show the efficiency of this method.

In this paper, a super-resolution method based on the Discrete Cosine Transform (DCT) is proposed for a signal with some frequency damage. If the damage process can be modeled as linear convolutoin with a type 1 linear phase FIR filter, it is shown that some DCT coefficients of the damaged signal are the same as those of the original signal except for the DCT coefficients corresponding to the frequency damage. From this investigation, the proposed method is provided for the DCTs with four types as expanding the super-resolution method based on the Discrete Fourier Transform (DFT). In addition,two magnification approaches based on the proposed method are described to improve the conventional approach.