## 1 Introduction  
The spectra, textures, and geometry of high-resolution remote sensing images are very rich, and different features describe ground objects from different angles [1, 2]. To give full play to the advantages of the spectral, texture, and geometric features of high-resolution remote sensing images, object-oriented classification usually allows more features to participate in classification. If all features participate in classification, the processing speed is greatly reduced, while the classification accuracy is reduced in the case of limited training samples [3, 4]. Therefore, how to select the optimal features from the feature space to participate in classification is the primary problem to be solved in the field of high-resolution image object-oriented classification [5, 6]. Feature selection is an important task in data mining and machine learning and can effectively reduce the dimension of data and improve the performance of algorithms [7, 8]. With the increase in data, feature selection has become an indispensable part of data processing [9]. The purpose of feature selection is to remove irrelevant or redundant features, retain useful features, and obtain appropriate feature subsets [10]. Feature selection methods can be divided into three series: filter, wrapper, and embedded [11]. Among them, filter methods directly evaluate the statistical performance of all the training data, as this is independent of the subsequent learning algorithm. Although it has the advantage of fast speed, it has a large performance deviation from the subsequent learning algorithm and is not effective when considering big data features [12]. Wrapper methods evaluate a subset of features with respect to the training accuracy of the subsequent learning algorithm and have the advantage of small deviation, but this type of method is large in size and involves significant computational burden [13]. Embedded methods combine the advantages of the above methods to some extent, but the difficulty with this type of method is the need to construct a suitable function optimization model [14]. From the above analysis, it is clear that the various types of methods have limitations in feature selection. In order to address these limitations, we selected the Fisher Score [15; 16] and mRMR [17] as filter methods for comparison with decision tree [18; 19] and random forest methods, respectively. The RF [20; 21], \(k\)-nearest neighbors (kNN) and support vector machine (SVM) approaches were combined for image classification. Filter methods can be divided into unsupervised, semi-supervised, and supervised feature selection methods [22; 23]. At present, supervised feature selection methods include Relief-F [24], mRMR, and Fisher Score. The Relief-F algorithm is a typical filtered feature optimization algorithm, which calculates the weights of feature variables, ranks them, and then extracts the optimal set of features. The Relief-F algorithm is highly efficient and suitable for most data. The mRMR algorithm is a feature optimization method based on mutual information theory, which is used to maximize the correlation between a selected feature subset and the category, while ensuring that the redundancy between the selected features is as small as possible [25; 26]. The Fisher Score is an effective criterion for judging the sample features, derived from [PERSON]'s linear discriminant, which finds feature subsets in the feature set space that maximize the distance between different categories of data points while minimizing the distance between those in the same category. Based on the above, we chose to combine the Fisher Score and mRMR algorithm to downscale the feature space of remote sensing images, where the Fisher Score is used to calculate the ratio of the variance within each feature class and the variance between each feature class, while the mRMR algorithm is used to filter out those features with the greatest relevance to the target category and the least redundancy between them.