## 1 Introduction  
Currently, the spatial resolution of remote sensing images has reached the sub-meter level, and the high-resolution images contain more spectral characteristics of ground objects. However, it also adds certain disturbing noise to the recognition and information extraction of some ground objects. For example, the variation of tree canopy due to light will be averaged out with the reduction of resolution, and the reduction of resolution can instead improve the inter-class separability [1, 2]. This is because the images are enhanced, improving intra-class consistency and enhancing edges. Remote sensing image enhancement techniques can compress data and enhance features of ground objects, making the images easier to interpret manually or process by a machine. This is important for improving the segmentation accuracy of remote sensing images. At present, the image enhancement techniques of remote sensing images are divided into three major categories: One is the spatial domain method, which focuses on point-to-point manipulation of the image; the second category is the frequency domain method. Fourier transforms are used to transform the image into the domain of spatial frequency; and the third category is color enhancement. Color enhancement techniques transform the grey-level image into color image models, such as RGB, HIS and CMY, and for remote sensing images, it is mainly the HIS Transform and Pseudo-Color Transform [3; 4; 5; 6]. This study focuses on the spatial domain method, which is more concerned with point-to-point processing of remote sensing images, such as common NDVI, NDBI, linear stretching, PCA, TCT, etc. Recently, [PERSON] et al. proposed a deep learning fusion network DSLN that effectively utilizes NDVI to avoid the effects of few spectra, low inter-class separability and large intra-class variations in high-resolution images. The average OA and average Kappa coefficient of the classification accuracy reached 0.8069 and 0.7161, respectively, on the GF-1 dataset [7]. [PERSON] et al. improved the NDBI and proposed the normalized spectral building index (NSBI) and differential spectral building index (DSBI), and both proposed indices achieved good enhancement results for different building conditions [8]. Furthermore, [PERSON] and [PERSON] addressed the problem of the low accuracy of urban impervious surface extraction by traditional linear spectral mixture analysis (LSMA) and improved LSMA by combining high-resolution remote sensing images, PCA and NDBI, and the improved LSMA extracted better accuracy of the impervious surface area [9]. [PERSON] et al. used BR-Net to train GF-2 images with PCA dimensionality reduction for building recognition and extraction, and not only is the recognition accuracy higher than the traditional machine learning model, but also the extracted buildings are more complete with clear boundaries [10]. TCT is an empirical orthogonal linear transformation of images made by [PERSON] and [PERSON] in 1976 and is based on the structure of the information distribution of the spectral information of soil and vegetation in multidimensional space, named because of the \"tassel hat\" shape of the spectra after the orthogonal linear transformation [11]. As the components formed by the TCT are not only independent of each other but also can better distinguish the main ground objects, the TCT is widely used in various fields. For instance, [PERSON] et al. successfully extracted coastline using the greenness component and moisture component of the TCT with producer and user accuracy of 0.95 and 0.91, respectively, and higher accuracy for shorelines in areas with high sand content [12]. [PERSON] et al. monitored the desertification process on the Mongolian plateau based on MODIS images processed by TCT and TGSI time series, which provided a new method for desertification research [13]. [PERSON] et al. successfully achieved a high-precision extraction of water bodies using GF-1 images processed by TCT to attenuate the effects of shadows and dense vegetation [14]. [PERSON] et al.