High-quality road network information plays a vital role in regional economic development, disaster emergency management and land planning. To date, studies have primarily focused on sampling flat urban roads, while fewer have paid attention to road extraction in mountainous regions. Compared with road extraction in flat regions, road extraction in mountainous regions suffers more interference, due to shadows caused by mountains and road-like terrain. Furthermore, there are more practical problems involved when researching an entire region rather than at the sample level. To address the difficulties outlined regarding mountain road extraction, this paper takes Jiuzhaigou county in China as an example and studies road extraction in practical applications. Based on deep learning methods, we used a multistage optimization method to improve the extraction effect. First, we used the contrast limited adaptive histogram equalization (CLAHE) algorithm to attenuate the influence of mountain shadows and improve the quality of the image. Then the road was extracted by the improved DSDNet network. Finally, the terrain constraint method is used to reduce the false detection problem caused by the terrain factor, and after that the final road extraction result is obtained. To evaluate the effect of road extraction comprehensively, we used multiple data sources (i.e., points, raster and OpenStreetMap data) in different evaluation schemes to verify the accuracy of the road extraction results. The accuracy of our method for the three schemes was 0.8631, 0.8558 and 0.8801, which is higher than other methods have obtained. The results show that our method can effectively solve the interference of shadow and terrain encountered in road extraction over mountainous regions, significantly improving the effect of road extraction.

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture.

Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

Accurate and reliable horticultural crop classification results are an important part of agricultural management. At present, research on orchard classification based on optical images in complex mountain areas is vulnerable to the influence of cloudy weather, especially in the summer, which leads to a lack of key phenological characteristics. To solve this problem, a parcel-level orchard mapping experiment with an irregular time series was carried out in Qixia City, China. Firstly, the candidate parcels in the study area were extracted from VHR images with a spatial resolution of 0.55 m based on RCF and DABNet models. The F1 score and area-based intersection-over-union (IoU) of the parcel extraction results were calculated. When the boundary buffer radius was 1 m, the F1 score was 0.93. When the radius was 2 m, the F1 score was 0.96. The IoU was 0.872, which shows the effectiveness of the parcel extraction method. Then, based on Sentinel-2 data, the NDVI, EVI, and SAVI vegetation indexes were calculated to construct an irregular time series. A two-dimensional CNN model was used for classification. In order to verify the effectiveness of this method, the study also constructed regular time series for the study area and conducted classification experiments using the 2DCNN and LSTM as classifiers, respectively. Confusion matrices were constructed for the classification results, and the overall accuracy was calculated. The results show that the overall accuracy of the method based on irregular time series is 97.76%, with a kappa coefficient of 0.96, higher than the other experiments, which indicates that the classification method based on irregular time series is effective and can make full use of the fragmented spectral features. Finally, based on the candidate parcels and pixel-level classification results of the study area, the crop categories of the parcels were filled to achieve accurate parcel-level mapping of horticultural crops in complex mountain areas. This method can provide a theoretical reference for orchard crop mapping and serves the development of regional precision agriculture.