## 1 Introduction
combined TCT and support vector machines (SVMs) for land cover classification of Landsat 8 OLI images [15]. [PERSON] et al. used partial least squares regression (PLSR) to estimate the leaf area index (LAI) by adding the greenness component of TCT to achieve an estimate of LAI with a minimum number of features [16]. [PERSON] et al. proposed a method to extract the flooded area of farmland based on TCT, to solve the low accuracy of traditional water body extraction methods due to the high-water content of crops after disasters [17]. The application of TCT has long gone beyond the initial proposal for crop growth monitoring, but the existing applications on TCT, although with high accuracy, lack controlled experiments. The TCT uses the transpose of the TCT coefficients to achieve an orthogonal linear transformation of the image, while the orthogonal linear transformation of the image by the TCT coefficients without transposition and further enhanced by linear stretching and percentage truncation stretching (PTS) is the pseudo-TCT [18; 19; 20; 21; 22]. Currently, the assessment of the effects of image enhancement algorithms are mainly divided into two categories, one is the subjective image quality assessment (IQA), in which the image quality is observed and assessed manually; the other is the objective IQA, which uses algorithms to assess the image quality [23; 24]. The objective IQA can be divided into three categories according to their dependence on the high-quality (HQ) reference image: the first category is the full-reference (FR) IQA, using all the information of the HQ reference image for IQA; the second category is reduced-reference (RR) IQA, which only requires comparison with some statistical features of the HQ reference image; and the third category is no-reference (NR) IQA, which does not require the HQ reference image [25; 26; 27; 28; 29; 30; 31]. [PERSON] et al. proposed an NR IQA method based on a deep convolutional neural network (CNN). They evaluated the network on the LIVE database and achieved a liner Person correction that outperformed state-of-the-art reference image IQA methods [32]. [PERSON] et al. developed an efficient general-purpose NR IQA model, dubbed spatial-spectral entropy-based quality (SSEQ) index [33]. Although FR IQA and RR IQA are easier to use and more sophisticated, the HQ images cannot be obtained as reference images in many cases. To test the effects of the pseudo-TCT, we also assessed the pseudo-TCT image quality from both subjective IQA and objective IQA. Although the TCT coefficients of IKONOS were proposed in 2003, its property of making ground objects, such as man-made objects, water and vegetation, more visible was only analyzed in terms of brightness. We have not only proposed the pseudo-TCT but also designed a series of experiments to verify the image enhancement effects of the pseudo-TCT. We compared the visual effects of different ground objects in different images to determine whether the contrast between the ground objects increased. To examine the separability of typical ground objects from pseudo-TCT images, K-Means and ISODATA clustering were used to examine the separability of buildings and roads from background ground objects [34; 35; 36; 37]. Additionally, the spectral characteristics of each typical ground object were visualized by 3D visualization. To verify whether the intra-class coherence and edges of the ground objects were enhanced, the intra-class coherence was visualized by calculating the mean Euclidean distances between the pixel values of each point and the pixel values of eight neighboring points. In addition, we graded the intra-class coherence display to make the effect of edge enhancement clearer and also calculated the standard deviation metric in the NR IQA of the intra-class consistency images. The experimental results showed that the pseudo-TCT can enhance the GF-2 image, increase the separation of typical ground objects and make the edges of typical ground objects clearer.