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Summary: Despite medical advances, there remains an unmet need for better treatment of obesity. Itaconate, a product of the decarboxylation of the tricarboxylic acid cycle intermediate cis-aconitate, plays a regulatory role in both metabolism and immunity. Here, we show that itaconate, as an endogenous compound, counteracts high-fat-diet (HFD)-induced obesity through leptin-independent mechanisms in three mouse models. Specifically, itaconate reduces weight gain, reverses hyperlipidemia, and improves glucose tolerance in HFD-fed mice. Additionally, itaconate enhances energy expenditure and the thermogenic capacity of brown adipose tissue (BAT). Unbiased proteomic analysis reveals that itaconate upregulates key proteins involved in fatty acid oxidation and represses the expression of lipogenic genes. Itaconate may provoke a major metabolic reprogramming by inducing fatty acid oxidation and suppression of fatty acid synthesis in BAT. These findings highlight itaconate as a potential activator of BAT-mediated thermogenesis and a promising candidate for anti-obesity therapy.

The effective and rapid acquisition of wide-area mine occupation information is crucial for ecological geo-environmental protection and sustainable development. Remote sensing instance segmentation technology based on deep learning is a promising solution. However, there are two significant challenges including insufficient training datasets and unsuitable segmentation models. To overcome these issues, this study provides a large-scale remote sensing instance segmentation dataset for mining land occupation (CUG_MISDataset). The CUG_MISDataset comprises 1426 image blocks and more than 3000 instances, covering all 150 types of mines found in China's Hubei province. It features multiple mine types, various land occupations, and complex instance scales. First, this study compares the performance of seven mainstream remote sensing instance segmentation models using the proposed CUG_MISDataset. The results show that all seven models achieve high segmentation accuracy. It indicates that the constructed CUG_MISDataset is robust and can serve as a valuable benchmark for remote sensing instance segmentation of mining areas. Second, aiming at the difficulty of large scale variation in this dataset, we propose a multiscale dilation feature pyramid network (MSD-FPN), which introduces a dynamic weight allocation mechanism to give more weight to important semantic information, while convolution with different dilation rates is used in the module to enhance the expression of mines’ multiscale features. The proposed MSD-FPN can achieve a 2.0% average precision improvement on the CUG_MISDataset.

Abstract Background Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC–MS/MS)-based measurements among different instruments and laboratories. Results Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC–MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. Conclusion Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

Monitoring the land covers in complex landscapes is of great significance for the sustainable development of mine geo-environments. As most existing remote sensing scene datasets are composed of RGB images, there is a lack of multimodal datasets for complex landscapes with mining land covers (MLCs) at a fine-scale. In this study, a new dataset was created by the China University of Geosciences (CUG), Wuhan (named CUG-MLCs) using ZiYuan-3 imagery-based multispectral and topographic data. Moreover, the characteristics of multisize objects, irregular or blurred edges, and spectral-spatial-topographic heterogeneity and variability limited the classification accuracy. Therefore, an edge enhanced channel attention-based graph convolution network (ECA-GCN) was proposed and tested. The proposed ECA-GCN includes three key modules. 1) Multiscale and shallow feature fusion, used to fuse the multiscale convolutional features and shallow features, which helps present the MLC features with various scales; 2) edge enhanced channel attention, used to further select effective channels after a spatial edge feature enhancement, which helps identify irregular or blurred MLCs; and 3) edge detection-based GCN, used for edge feature-based adjacency matrix and feature maps from (2) to construct GCN, which can obtain edge node relation and global contextual information. This framework improved the representation of complex landscape characteristics. The proposed ECA-GCN achieved an overall accuracy of 66.60% ± 1.39%, averaged accuracy of 36.25% ± 1.50%, and Kappa of 55.91% ± 2.05%, thus, outperforming other models. In general, the proposed dataset and model were positive for the fine classification of complex landscapes.

Deep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of the models to extract multiscale features and global features on surface objects of complex scenes is currently insufficient. We propose a framework based on global context spatial attention (GCSA) and densely connected convolutional networks to extract multiscale global scene features, called GCSANet. The mixup operation is used to enhance the spatial mixed data of remote sensing images, and the discrete sample space is rendered continuous to improve the smoothness in the neighborhood of the data space. The characteristics of multiscale surface objects are extracted, and their internal dense connection is strengthened by the densely connected backbone network. GCSA is introduced into the densely connected backbone network to encode the context information of the remote sensing scene image into the local features. Experiments were performed on four remote sensing scene datasets to evaluate the performance of GCSANet. The GCSANet achieved the highest classification precision on AID and NWPU datasets and the second-best performance on the UC Merced dataset, indicating the GCSANet can effectively extract the global features of remote sensing images. In addition, the GCSANet presents the highest classification accuracy on the constructed mountain image scene dataset. These results reveal that the GCSANet can effectively extract multiscale global scene features on complex remote sensing scenes. The source codes of this method can be foundin https://github.com/ShubingOuyangcug/GCSANet.

Owing to their powerful feature extraction capabilities, deep learning-based methods have achieved significant progress in hyperspectral remote sensing classification. However, several issues still exist in these methods, including a lack of hyperspectral datasets for specific complicated scenarios and the need to improve the classification accuracy of land cover with limited samples. Thus, to highlight and distinguish effective features, we propose a hyperspectral classification framework based on a joint channel-space attention mechanism and generative adversarial network (JAGAN). To relearn feature-based weights, a higher priority was assigned to important features, which was developed by integrating a two-joint channel-space attention model to obtain the most valuable feature via the attention weight map. Additionally, two classifiers were designed in JAGAN: sigmoid was used to determine whether the input data were real or fake samples produced by the generator, while Softmax was adopted as a land cover classifier to yield the prediction type labels of the input samples. To test the classification performance of the JAGAN model, we used a self-constructed complex land cover dataset based on GaoFen-5 AHSI images, which consists of mixed landscapes of mining and agricultural areas from the urban-rural fringe. Compared with other methods, the proposed model achieved the highest overall classification accuracy of 86.09%, the highest kappa amount of 79.41%, the highest F1 score of 85.86%, and the highest average accuracy of 82.30%, indicating the JAGAN can effectively improve the classification accuracy for limited samples in complex regional environments using GF-5 AHSI images.

The development of domestic high resolution remote sensing satellite technology provides a new means for the investigation of macroscopic, local and detailed characteristics of the Quaternary.Alluvial-diluvial fans, valley terraces, glacial deposits are widely distributed in the eastern section of China-Pakistan Economic Corridor. In order to study the applicability of domestic high-resolution data in the Quaternary geological survey of this region, the alluvial and flood fan in front of Uruat mountain along Gaizi River basin of China-Pakistan highway and three typical Quaternary strata of Gaizi River valley terrace and Kraguirake glacial moraine were selected as the research objects in this study.Using domestic GF-2 images as the main data sources, with the aid of digital elevation model data, the remote sensing interpretation marker was established, and the fine geological interpretation was carried out.The composition and variation were found out through field verification.The previous distribution range of different periods of glacial moraines in Kailaillak was revised.Based on the analysis of the terrace in Gaizi River valley, the study area experienced at least five stages of tectonic uplift since late Epipleistocene, and the terrace base was formed by at least two stages of glaciation.This study shows that GF-2 images can not only quickly identify the change characteristics of landforms and loose deposits at macro scale, but also it can get geological phenomena that cannot be observed by conventional methods.And then it can meet the requirements of large scale interpretation and cartography.Especially in the identification of micro-geomorphology and interpretation of Quaternary strata, it can improve the level of fine geological interpretation.Research results can provide basic geological data for understanding the development and evolution process of fluvial and glaciation in the Gaizi River basin.Besides, this study can provide a typical example for remote sensing investigation of Quaternary soil mass along the China-Pakistan highway.

In recent years, with the progress of computer technology, some traditional industries such as geology are facing changes in industrial structure and application mode. So we try to apply big data and virtualization technology in the field of geoscience. This study aims at addressing the existing problems in geological applications, such as data sharing, data processing and computing performance. A Geological Cloud Platform has been designed and realized preliminarily with big data and virtualization technology. The application of the Geological Cloud Platform can be divided into two parts: 1) to nest the geological computing model in cloud platform and visualize the results and 2) to use relevant software to conduct data analysis and processing in virtual machines of Windows or Linux system. Finally, we prospect Carlin-type deposits in Nevada by using the spatial data model ArcSDM in the virtual machine.

Remote sensing image scene classification has been widely applied and has attracted increasing attention. Recently, convolutional neural networks (CNNs) have achieved remarkable results in scene classification. However, scene images have complex semantic relationships between multiscale ground objects, and the traditional stacked network structure lacks the ability to effectively extract multiscale and key features, resulting in limited feature representation capabilities. By simulating the way that humans understand and perceive images, attention mechanisms can be beneficial for quickly and accurately acquiring key features. In our study, we propose a channel-attention-based DenseNet (CAD) network for scene classification. First, the lightweight DenseNet121 is selected as the backbone for the spatial relationship between multiscale ground objects. In the spatial domain, densely connected CNN layers can extract spatial features at multiple scales and correlate with each other. Second, in the channel domain, a channel attention mechanism is introduced to strengthen the weights of the important feature channels adaptively and to suppress the secondary feature channels. Third, the cross-entropy loss function based on label smoothing is used to reduce the impact of interclass similarity upon feature representations. The proposed CAD network is evaluated on three public datasets. The experimental results demonstrate that the CAD network can achieve performance comparable to those of other state-of-the-art methods. The visualization through the Grad-CAM ++ algorithm also reflects the effectiveness of channel attention and the powerful feature representation capabilities of the CAD network.

Abstract DNA modifications, represented by 5‐methylcytosine (5mC), 5‐hydroxymethylcytosine (5hmC), 5‐formylcytosine (5fC), and 5‐carboxylcytosine (5caC), play important roles in epigenetic regulation of biological processes. The specific recognition of DNA modifications by the transcriptional protein machinery is thought to be a potential mechanism for epigenetic‐driven gene regulation, and many modified DNA‐specific binding proteins have been uncovered. However, the panoramic view of the roles of DNA modification readers at the proteome level remains largely unclear. Here, a recently developed concatenated tandem array of consensus transcription factor (TF) response elements (catTFREs) approach is employed to profile the binding activity of TFs at DNA modifications. Modified DNA‐binding activity is quantified for 1039 TFs, representing 70% of the TFs in the human genome. Additionally, the modified DNA‐binding activity of 600 TFs is monitored during the mouse brain development from the embryo to the adult stages. Readers of these DNA modifications are predicted, and the hierarchical networks between the transcriptional protein machinery and modified DNA are described. It is further demonstrated that ZNF24 and ZSCAN21 are potential readers of 5fC‐modified DNA. This study provides a landscape of TF–DNA modification interactions that can be used to elucidate the epigenetic‐related transcriptional regulation mechanisms under physiological conditions.

The human stomach is divided into seven anatomically distinct regions but their protein composition is largely unknown. Here, the authors present a region-resolved map of the healthy human stomach mucosa as well as mucosa proteomes of tumor and tumor nearby tissue from gastric cancer patients.

Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments.

The mammalian stomach is a complex organ with diverse roles in health and disease. Here, the authors integrate proteomics and transcriptomics to analyze the mouse stomach at 15 time points during development, providing molecular level insights into system-wide developmental changes.

Classification of remote sensing scene image (RSSI) has been broadly applied and has attracted increasing attention. However, scene classification methods based on convolutional neural networks (CNNs) require a large number of manually labeled samples as training data, which is time-consuming and costly. Therefore, generating labeled data becomes a practical approach. However, conventional scene generation based on generative adversarial networks (GANs) involve some significant limitations, such as distortion and limited size. To solve the mentioned problems, herein, we propose a method of RSSI generation using element geometric transformation and GAN-based texture synthesis. Firstly, we segment the RSSI, extracting the element information in the RSSI. Then, we perform geometric transformations on the elements and extract the texture information in them. After that, we use the GAN-based method to model and generate the texture. Finally, we fuse the transformed elements with the generated texture to obtain the generated RSSI. The geometric transformation increases the complexity of the scene. The GAN-based texture synthesis ensures the generated scene image is not distorted. Experimental results demonstrate that the RSSI generated by our method achieved a better visual effect than a GAN model. In addition, the performance of CNN classifiers was reduced by 0.44–3.41% on the enhanced data set, which is partly attributed to the complexity of the generated samples. The proposed method was able to generate diverse scene data with sufficient fidelity under conditions of small sample size and solve the accuracy saturation issues of the public scene data sets.

Fine land cover classification (FLCC) of complex landscapes is a popular and challenging task in the remote sensing community. In complex surface-mined areas (CSMAs), researchers have conducted FLCC using traditional machine learning methods and deep learning algorithms. However, convolutional neural network (CNN) algorithms that may be useful for FLCC of CSMAs have not been fully investigated. This study proposes a multimodal remote sensing data and multiscale kernel-based multistream CNN (3M-CNN) model. Experiments based on two ZiYuan-3 (ZY-3) satellite imageries of different times and seasons were conducted in Wuhan, China. The 3M-CNN model had three main features: (1) multimodal data-based multistream CNNs, i.e., using ZY-3 imagery-derived true color, false color, and digital elevation model data to form three CNNs; (2) multisize neighbors, i.e., using different neighbors of optical and topographic data as inputs; and (3) multiscale convolution flows revised from an inception module for optical and topographic data. Results showed that the proposed 3M-CNN model achieved excellent overall accuracies on two different images, and outperformed other comparative models. In particular, the 3M-CNN model yielded obvious better visual performances. In general, the proposed process was beneficial for the FLCC of complex landscape areas.

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.

Land cover classification (LCC) of complex landscapes is attractive to the remote sensing community but poses great challenges. In complex open pit mining and agricultural development landscapes (CMALs), the landscape-specific characteristics limit the accuracy of LCC. The combination of traditional feature engineering and machine learning algorithms (MLAs) is not sufficient for LCC in CMALs. Deep belief network (DBN) methods achieved success in some remote sensing applications because of their excellent unsupervised learning ability in feature extraction. The usability of DBN has not been investigated in terms of LCC of complex landscapes and integrating multimodal inputs. A novel multimodal and multi-model deep fusion strategy based on DBN was developed and tested for fine LCC (FLCC) of CMALs in a 109.4 km2 area of Wuhan City, China. First, low-level and multimodal spectral−spatial and topographic features derived from ZiYuan-3 imagery were extracted and fused. The features were then input into a DBN for deep feature learning. The developed features were fed to random forest and support vector machine (SVM) algorithms for classification. Experiments were conducted that compared the deep features with the softmax function and low-level features with MLAs. Five groups of training, validation, and test sets were performed with some spatial auto-correlations. A spatially independent test set and generalized McNemar tests were also employed to assess the accuracy. The fused model of DBN-SVM achieved overall accuracies (OAs) of 94.74% ± 0.35% and 81.14% in FLCC and LCC, respectively, which significantly outperformed almost all other models. From this model, only three of the twenty land covers achieved OAs below 90%. In general, the developed model can contribute to FLCC and LCC in CMALs, and more deep learning algorithm-based models should be investigated in future for the application of FLCC and LCC in complex landscapes.

For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

Based on the digital surface model (DSM) and jump point search (JPS) algorithm, this study proposed a novel approach to detect the optimal seamline for orthoimage mosaicking. By threshold segmentation, DSM was first identified as ground regions and obstacle regions (e.g., buildings, trees, and cars). Then, the mathematical morphology method was used to make the edge of obstacles more prominent. Subsequently, the processed DSM was considered as a uniform-cost grid map, and the JPS algorithm was improved and employed to search for key jump points in the map. Meanwhile, the jump points would be evaluated according to an optimized function, finally generating a minimum cost path as the optimal seamline. Furthermore, the search strategy was modified to avoid search failure when the search map was completely blocked by obstacles in the search direction. Comparison of the proposed method and the Dijkstra’s algorithm was carried out based on two groups of image data with different characteristics. Results showed the following: (1) the proposed method could detect better seamlines near the centerlines of the overlap regions, crossing far fewer ground objects; (2) the efficiency and resource consumption were greatly improved since the improved JPS algorithm skips many image pixels without them being explicitly evaluated. In general, based on DSM, the proposed method combining threshold segmentation, mathematical morphology, and improved JPS algorithms was helpful for detecting the optimal seamline for orthoimage mosaicking.

Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

Land cover classification (LCC) in complex surface-mined landscapes has become very important for understanding the influence of mining activities on the regional geo-environment. There are three characteristics of complex surface-mined areas limiting LCC: significant three-dimensional terrain, strong temporal-spatial variability of surface cover, and spectral-spatial homogeneity. Thus, determining effective feature sets are very important as input dataset to improve detailed extent of classification schemes and classification accuracy. In this study, data such as various feature sets derived from ZiYuan-3 stereo satellite imagery, a feature subset resulting from a feature selection (FS) procedure, training data polygons, and test sample sets were firstly obtained; then, feature sets’ effects on classification accuracy was assessed based on different feature set combination schemes, a FS procedure, and random forest algorithm. The following conclusions were drawn. (1) The importance of feature set could be divided into three grades: the vegetation index (VI), principal component bands (PCs), mean filters (Mean), standard deviation filters (StDev), texture measures (Textures), and topographic variables (TVs) were important; the Gaussian low-pass filters (GLP) was just positive; and none were useless. The descending order of their importance was TVs, StDev, Textures, Mean, PCs, VI, and GLP. (2) TVs and StDev both significantly outperformed VI, PCs, GLP, and Mean; Mean outperformed GLP; all other pairs of feature sets had no difference. In general, the study assessed different feature sets’ effects on LCC in complex surface-mined landscapes.

Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex landscapes. The present study focused on LCM in such a mixed complex landscape located in Wuhan City, China. A procedure combining ZiYuan-3 (ZY-3) stereo satellite imagery, the feature selection (FS) method, and machine learning algorithms (MLAs) (random forest, RF; support vector machine, SVM; artificial neural network, ANN) was proposed and first examined for both LCM of surface-mined and agricultural landscapes (MSMAL) and classification of surface-mined land (CSML), respectively. The mean and standard deviation filters of spectral bands and topographic features derived from ZY-3 stereo images were newly introduced. Comparisons of three MLAs, including their sensitivities to FS and whether FS resulted in significant influences, were conducted for the first time in the present study. The following conclusions are drawn. Textures were of little use, and the novel features contributed to improve classification accuracy. Regarding the influence of FS: FS substantially reduced feature set (by 68% for MSMAL and 87% for CSML), and often improved classification accuracies (with an average value of 4.48% for MSMAL using three MLAs, and 11.39% for CSML using RF and SVM); FS showed statistically significant improvements except for ANN-based MSMAL; SVM was most sensitive to FS, followed by ANN and RF. Regarding comparisons of MLAs: for MSMAL based on feature subset, RF achieved the greatest overall accuracy of 77.57%, followed by SVM and ANN; for CSML, SVM had the highest accuracies (87.34%), followed by RF and ANN; based on the feature subsets, significant differences were observed for MSMAL and CSML using any pair of MLAs. In general, the proposed approach can contribute to LCM in complex surface-mined and agricultural landscapes.