Extracting the information on sloping arable land based on improved DexiNed and DeepLabv3+ networks in a cascade connection
LIU Chaobing1(), GAN Shu1,2(), YUAN Xiping3,4, SHANG Huasheng1
1. School of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China 2. Application Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province, Kunmin 650093,China 3. School of Earth Sciences and Engineering, West Yunnan University of Applied Sciences, Dali 671006, China 4. Key Laboratory of Mountain Real Scene Point Cloud Data Processing and Application for Universities in Yunnan Province, Dali 671006, China
Arable land in hilly and mountainous areas exhibits small, narrow, and complex structures with blurred boundaries, posing challenges in extracting arable land information quickly and accurately. Hence, this study proposed a model for extracting the information on sloping arable land based on improved DexiNed and DeepLabv3+ networks in a cascade connection. First, the backbone network of the DeepLabv3+ model uses MobileNetV2 to replace the original Xception model. A closely related low-level information extraction method preliminarily fuses the lower- and higher-level information as the input of the original low-level information. Second, the original atrous spatial pyramid pooling (ASPP) module of the DeepLabv3+ model is optimized through dilated convolution, with dilation rate values of 2, 4, 8, and 16. Third, cascaded edge detection technology enables the interconnection of arable land edges and semantic features. The proposed model was applied to extract information on arable land in the Lufeng Dinosaur Valley in Yunnan Province using the GF-2 image as the data source. The results show that the proposed model with an improved architecture and algorithm identified the arable land more accurately, with the extraction results closely matching the image with real arable land annotated. With reduced extraction missing and errors, the proposed model exhibits enhanced accuracy and stability overall.
刘超兵, 甘淑, 袁希平, 尚华胜. 级联改进DexiNed和DeepLabv3+网络的坡耕地提取[J]. 自然资源遥感, 2025, 37(2): 49-55.
LIU Chaobing, GAN Shu, YUAN Xiping, SHANG Huasheng. Extracting the information on sloping arable land based on improved DexiNed and DeepLabv3+ networks in a cascade connection. Remote Sensing for Natural Resources, 2025, 37(2): 49-55.
Chen Z X, Ren J Q, Tang H J, et al. Progress and perspectives on agricultural remote sensing research and applications in China[J]. Journal of Remote Sensing, 2016, 20(5):748-767.
Shi Z, Liang Z Z, Yang Y Y, et al. Status and prospect of agricultural remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(2):247-260.
Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4):0428001.
Zhang X C, Huang J F, Ning T. Progress and prospect of cultivated land extraction from high-resolution remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10):1582-1590.
Xiong X L, Hu Y M, Wen N, et al. Progress and prospect of cultivated land extraction research using remote sensing[J]. Journal of Agricultural Resources and Environment, 2020, 37(6):856-865.
Li A N, Bian J H, Zhang Z J, et al. Progresses,opportunities,and challenges of mountain remote sensing research[J]. Journal of Remote Sensing, 2016, 20(5):1199-1215.
Zhou N, Yang P, Wei C S, et al. Accurate extraction method for cropland in mountainous areas based on field parcel[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(19):260-266.
[9]
Dong S, Wang P, Abbas K. A survey on deep learning and its applications[J]. Computer Science Review, 2021, 40:100379.
[10]
Ma L, Liu Y, Zhang X, et al. Deep learning in remote sensing applications:A meta-analysis and review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152:166-177.
Liu W, Wu Z F, Luo J C, et al. A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1):105-116.
doi: 10.11947/j.AGCS.2021.20190448
[12]
Xie S, Tu Z. Holistically-nested edge detection[C]// 2015 IEEE International Conference on Computer Vision (ICCV).Santiago,Chile.IEEE, 2015:1395-1403.
[13]
Zhao W D, Zhang Y, Zhang D D, et al. Refined edge detection model based on RCF[J]. Journal of Measurement Science and Instrumentation, 2024, 15(2):195-203.
[14]
Zhou L, Zhang C, Wu M. D-LinkNet:LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).Salt Lake City,UT,USA.IEEE, 2018:192-1924.
[15]
Ronneberger O, Fischer P, Brox T. U-net:Convolutional networks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham:Springer, 2015:234-241.
[16]
Xia L, Luo J, Sun Y, et al. Deep extraction of cropland parcels from very high-resolution remotely sensed imagery[C]// 2018 7th International Conference on Agro-Geoinformatics (Agro-geoinformatics). Hangzhou,China.IEEE, 2018:1-5.
[17]
Diao Z, Guo P, Zhang B, et al. Maize crop row recognition algorithm based on improved UNet network[J]. Computers and Electronics in Agriculture, 2023, 210:107940.
[18]
Liu S, Liu L, Xu F, et al. A deep learning method for individual arable field (IAF) extraction with cross-domain adversarial capabi-lity[J]. Computers and Electronics in Agriculture, 2022, 203:107473.
[19]
Zhu M, Yao M, He Y, et al. Studies on high-resolution remote sensing sugarcane field[J]. IOP Conference Series: Earth and environmental science, 2019, 237: 32046.
Gao S, Yuan X P, Gan S, et al. Experimental analysis of spatial feature detection of the ring geomorphology at the south edge of Lufeng Dinosaur Valley based on UAV imaging point cloud[J]. Bulletin of Geological Science and Technology, 2021, 40(6):283-292.
Chen J J. Extraction of cultivated land information in hilly area of East Sichuan based on GF-2 satellite image[D]. Chengdu: Chengdu University of Technology, 2017.
[22]
Sandler M, Howard A, Zhu M, et al. MobileNetV2:Inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA.IEEE, 2018:4510-4520.
[23]
Chollet F. Xception:Deep learning with depthwise separable convolutions[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu,HI,USA.IEEE, 2017:1800-1807.
[24]
Soria X, Sappa A, Humanante P, et al. Dense extreme inception network for edge detection[J]. Pattern Recognition, 2023, 139:109461.
2025, Vol. 37 
), 甘淑1,2(
