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.

The borehole log image contains valuable text information, encompassing key geological data such as structural composition, orebody distribution, and lithological characteristics. These data are important for mineral prediction, GeoBigData, and GeoModeling. However, text recognition in borehole log images is challenging due to complex structures, image noise, and diverse fonts, leading to low accuracy with traditional OCR methods. As a result, substantial manual intervention is often required for verification and correction, hindering efficient application. This study proposes an optimization method based on the multi-layout adjustment voting mechanism to improve text recognition accuracy in borehole log images. During the recognition process, multiple OCR results are generated by adjusting text layouts, and a voting mechanism integrates these results to produce the most accurate output. Experimental results on the Dayingezhuang and Dingjiashan datasets demonstrate the effectiveness of the proposed method, achieving F1 scores of 97.96% and 94.36%, respectively. This optimization method improves text recognition accuracy and recall without modifying the OCR algorithm or applying post-processing, providing a new technical approach to enhancing text recognition precision in borehole log images. This improvement in text extraction accuracy from geological borehole data not only facilitates large-scale integration and analysis of subsurface geological information but also provides essential foundational data for GeoBigData and GeoModeling applications.