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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (2) : 49-55     DOI: 10.6046/zrzyyg.2023332
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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
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Abstract  

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.
Keywords extraction of arable land information      edge detection      DeepLabv3+      hilly and mountainous areas     
ZTFLH:  TP79  
  P237  
Issue Date: 09 May 2025
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Articles by authors
Chaobing LIU
Shu GAN
Xiping YUAN
Huasheng SHANG
Cite this article:   
Chaobing LIU,Shu GAN,Xiping YUAN, et al. Extracting the information on sloping arable land based on improved DexiNed and DeepLabv3+ networks in a cascade connection[J]. Remote Sensing for Natural Resources, 2025, 37(2): 49-55.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023332     OR     https://www.gtzyyg.com/EN/Y2025/V37/I2/49
Fig.1  Research area
Fig.2  Label examples
Fig. 3  Improved DeepLabv3+ model
Fig.4  MobileNetv2 network module operation
输入 操作 通道数 瓶颈层重
复次数		 步幅
512×512×3 卷积 32 1 2
256×256×32 瓶颈层 16 1 1
256×256×16 瓶颈层 24 2 2
128×128×24 瓶颈层 32 3 2
64×64×32 瓶颈层 64 4 2
32×32×64 瓶颈层 96 3 1
32×32×96 瓶颈层 160 3 2
16×16×160 瓶颈层 — 1 1
Tab.1  Parameters of MobileNetv2 network module
Fig.5  DexiNed model structure
Fig.6  Cascade model network
序号 遥感图像 真实标注 本文方法 级联未
改进方法		 DeepLabv3+
方法		 本文方法
提取结果		 级联未改
进提取结果		 DeepLabv3+
提取结果
1
2
3
图例
Tab.2  Comparative analysis of cultivated land extraction results in mountainous areas
山地区 真实耕地
面积/ hm2		 误提面
积/hm2		 误提
率/%		 漏提面
积/ hm2		 漏提
率/%
级联改进模型 340.19 28.44 8.36 23.61 6.94
级联未改进模型 29.39 8.64 30.51 8.97
DeepLabv3+模型 30.48 8.96 32.93 9.68
Tab.3  Misextraction and omitting of cultivated land in mountainous areas
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