A Novel Remote Sensing Image Enhancement Method, the Pseudo-Tasseled Cap Transformation: Taking Buildings and Roads in GF-2 as an Example

• Published in: Applied sciences 2023-05, Vol.13 (11), p.6585
• Main Authors: Deng, Jiqiu, Dong, Wuzhou, Guo, Yiwei, Chen, Xiaoyan, Zhou, Renhao, Liu, Wenyi
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Buildings; buildings and roads; Classification; Clustering; Euclidean geometry; GF-2; Image enhancement; Image processing; intra-class consistency; K-Means; Land cover; Land use; Linear transformations; Machine learning; Methods; Pixels; pseudo-TCT; Remote sensing; Remote sensors; Vegetation; Visual effects
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13116585

With the improvements in sensor accuracy, the spectral features of high-resolution remote sensing images become more complex. As a result, the classification accuracy for land cover classification decreases. Remote sensing image enhancements can improve the visual effect and the intra-class consistency and enhance the characteristics of ground objects. These enhancements are important for both image interpretation and improving image segmentation accuracy. In this study, we propose a pseudo-tasseled cap transformation (pseudo-TCT) through an orthogonal linear transformation of Gaofen-2 (GF-2) images using the untransposed tasseled cap transformation (TCT) coefficients, and further, enhance the visual effect and the separability among ground objects by linear stretching and percentage truncation stretching. To examine the separability among ground objects in the pseudo-TCT image, we used K-Means clustering, ISODATA clustering and 3D visualization of the spectral features of typical ground objects. The results show that the separability of buildings and roads from background objects is better than in the original image and the TCT image, and typical ground objects are effectively distinguished. Additionally, we visualized intra-class consistency by calculating the mean Euclidean distance between the pixel values of each point and the pixel values of its eight neighboring points and calculated the standard deviation of the intra-class consistency images. The results indicate that the secondary textures of the objects were weakened, and edges were made clearer, enhancing intra-class consistency. The pseudo-TCT is effective, at least in our work, and could be a candidate for image enhancement under certain applications.