# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 1 Introduction  
Remote sensing images have become the main data source for obtaining land-use information at broad spatial scales. The most common method first assigns each pixel to a category using image segmentation and subsequently generates land-use information according to the pixel-by-pixel classification result [1; 2]. Since the accuracy of the final extraction is determined by the accuracy of the pixel-by-pixel classification, improving the segmentation accuracy is a common research focus [3]. The pixel feature extraction method and the classifier performance both have a decisive influence on segmentation results [4; 5]. Researchers have proposed a variety of methods to extract ideal features. For example, spectral indexes have been widely applied in the classification of low- and medium-spatial-resolution remote sensing images, as they can accurately reflect statistical information regarding pixel spectral values. Commonly used indexes include the vegetation index [6; 7; 8; 9; 10; 11; 12; 13], water index [14], normalized difference building index [15], ecological index [16], normalized difference vegetation index (NDVI) [10; 13], and derivative indexes such as the re-normalized difference vegetation index [12] or the growing season normalized difference vegetation index [13]. Taking account of the advantage of the short time period of low- and medium-spatial-resolution remote sensing images, some researchers have applied spectral indexes to time-series images [17; 18]. However, such indexes are mainly used to express common information within the same land-use type through simple band calculations. When these are applied to remote sensing images with rich detail and high spatial resolution, it becomes difficult to extract features with good discrimination, limiting the application of spectral indexes to the classification of such images. For high-spatial-resolution remote sensing images with a high level of detail, researchers initially proposed the use of texture features with the gray matrix method [19]. Subsequently, other researchers have proposed a series of methods for extracting more abundant texture features, including the Gabor filter [20], the Markov random field model [21], the Gibbs random field model [22], and the wavelet transform [23]. Compared with spectral index features, texture features can better express the spatial correlation between pixels, improve the ability to distinguish between features, and effectively improve the accuracy of pixel classification. Although the combination of spectral and texture features has greatly promoted the development of remote sensing image segmentation technology and significantly improved the accuracy of pixel classification results, the ongoing improvement in remote sensing technology has resulted in increasing image resolutions and higher levels of detail. Traditional texture feature extraction techniques now struggle with high-resolution images, such that new methods are needed to obtain effective feature information from such images [24; 25]. The developing field of machine learning is currently being applied to pixel feature extraction, with early applications to image processing, including neural networks [26; 27], support vector machines [28; 29], decision trees [30; 31], and random forests [32; 33]. These methods use pixel spectral information as inputs and achieve the desired feature results through complex calculations. Although these methods can fully explore the relationship between channels and obtain some effective feature information, these features only express the information of a single pixel rather than the spatial relationship between pixels, limiting these methods' application in the processing of remote sensing images. Convolutional neural networks (CNNs) use pixel blocks as inputs and compute them with a set of convolution kernels to obtain more features with a stronger distinguishing ability [34; 35; 36; 37].