## 5 Conclusions  
This study applied DOM and DEM data, superimposed constraints using maximum likelihood classification, and combined them with filter denoising and morphological operation to extract the Jiangdingya landslide area in complex terrain. The experimental results are analyzed as follows.  
The landslide areas were initially identified by combining the remote sensing image features of DOM data and the topographic impact factor in DEM data through using supervised classification and slope factor constraint analysis. This approach effectively eliminated the buildings, rivers, and roads that were erroneously buried by landslide deposits.  
The images were denoised using a threshold-based median filter and the OSTU method. Then, they were classified into binary images. Mathematical morphological operations including dilation, erosion, and opening were applied to extract information on the Jiangdingya landslide area. Furthermore, the morphology and texture of the landslide area were analyzed and evaluated, which resulted in accuracy, precision, and recall rates of 98.02%, 85.24%, and 84.78%, respectively. The experiment shows the effective elimination of errors in the classification of landslides in buildings, roads, bare land, and other areas. This exclusion eliminates interference factors caused by a large amount of sediment covering rich ground object types and validates the application value of the method in extracting landslides in complex terrain.  
In this study, samples are classified based on the terrain information of landslide occurrences in the study area. The characteristic information of terrain features is combined to select samples, which improves the accuracy of classification. Classification results are constrained with the assistance of DEM terrain information. Future research should consider relevant geological information and climate-influencing factors when classifying landslide states. InSAR technology has also been successfully applied to landslide extraction [37]. In parallel, this method will be verified in other typical landslide areas in future work.  
Conceptualization, Z.Q. and Y.N.; methodology, J.L. and Z.Q.; validation, J.L. and H.Q.; formal analysis, Y.W. and H.Q.; writing--original draft preparation, Y.N.; writing--review and editing, Z.Q., Y.W., and J.L.; project administration, Z.Q.; funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Scientific Research Project of Surveying and Mapping Geographic Information in Jiangsu Province under Grant JSCHKY201904; in part by the Marine Technology Brand Major of Jiangsu Province under Grant PPZY2015B116; by Jiangsu Ocean University's 2022 Higher Education Teaching Reform Research Project \"Research and Practice on Teaching Method Reform of 'Photogrammetry' Combining Virtual and Actual\" under Grant JGX2022045; and by the 2020 Jiangsu Province Graduate Practical Innovation Program Project under Grant SJCX20_1254; by Jiangsu Province College Student Innovation and Entrepreneurship Training Program under Grant SY202311641632001.  
Publicly available datasets were analyzed in this study. This data can be found here: [[[https://github.com/ljjf12345/data](https://github.com/ljjf12345/data)]([https://github.com/ljjf12345/data](https://github.com/ljjf12345/data))].  
The authors would like to show sincere thanks to those technicians who have contributed to this research and [PERSON]'s help in coordinating the experiment.  
The authors declare no conflict of interest.