Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

Automatic landslide detection based on very high spatial resolution (VHR) remote sensing images is crucial for disaster prevention and mitigation applications. With the rapid development of deep learning techniques, state-of-the-art semantic segmentation methods based on fully convolutional network (FCNN) have achieved outstanding performance in the landslide detection task. However, most of the existing studies only utilize visual features. Even if the advanced FCNN models are applied, there is still a certain amount of falsely detected and miss detected landslides. In this article, we innovatively introduce landslide susceptibility as prior knowledge and propose an innovative susceptibility-guided landslide detection method based on FCNN (SG-FCNN) to detect landslides from single temporal images. In addition, an unsupervised change detection method based on the mean changing magnitude of objects (MCMO) is further proposed and integrated with the SG-FCNN to detect newly occurred landslides from bi-temporal images. The effectiveness of the proposed SG-FCNN and MCMO has been tested in Lantau Island, Hong Kong. The experimental results show that the SG-FCNN can significantly reduce the amount of falsely detected and miss detected landslides compared with the FCNN. It can conclude that applying landslide susceptibility as prior knowledge is much more effective than using visual features only, which introduces a new methodology of landslide detection and lifts the detection performance to a new level.

In change detection, the pseudo-variations in the visual features of remote sensing images are attributed to imaging conditions, lighting, seasonal changes, atmospheric interference, and other factors. These pseudo-variations yield a great challenge to change detection. The traditional change detection network usually suffers from up-sampling information loss and blurred edges. Aiming at resolving the above problems, a network combining semantic flow and edge-aware refinement (SFEARNet) for highly efficient remote sensing image change detection has been proposed. The Pyramid Feature Enhancement Module (PFEM) has been designed for the enhancement of differential information. The introduction of the Semantic Flow Information Transmission Module (SFITM) enables the effective transmission and retaining of key information through semantic flow. An Edge-aware Refinement Module (EARM) has been developed, designed to extract change edge and enhance the refinement effect of the edge. The experiments have been conducted on the LEVIR-CD, WHU-CD, GZ-CD, and CLCD datasets. In comparison with the existing methodologies, the experimental results demonstrate that SFEARNet attains the highest change detection accuracy and the smallest flops while maintaining a similar number of parameters. This enables more efficient change detection. In particular, the proposed method can effectively refine the edges of the change region, reduce the loss of up-sampling information, and enhance differential feature extraction. This brings a new solution to the field of remote sensing image change detection. The code is available at https://github.com/miao-0417/SFEARNet.

The spatial arrangement of land-cover features constitutes different urban functional zone. With the same attributes of the urban functional zone, the land-cover features that make up the functional zone will have similar spatial distribution characteristics. Considering the importance of understanding spatial relationships between land-cover features, the up-bottom hierarchical decomposition and semantic understanding of functional zone are achieved. First, for object convolution neural network (OCNN)-based land cover classification, an equal-area dividing algorithm is proposed to automatically generate convolution kernel position. Second, to extract spatial relationship features of urban land covers, a novel distance weight-graph attention model (DW-GAM) is originally proposed for classifying urban functional zones by comparing the feature similarity of the land cover relationship graph. Third, considering the extreme difficulties in expressing the urban structure characteristic on a single scale, a recursive model that uses an urban road network of different levels to divide multiscale functional zones is built. Finally, taking the analysis of urban function allocation as the application objective, this article establishes a primary framework of the spatial pattern evaluation index. Experimental results conducted on a Google Earth image of Xi’an city show that the multiscale recursive model can accurately recognize urban functional zones by using the originally proposed DW-GAM. Then, based on the outcome of urban functional zone identification, the case study of urban function allocation analysis is innovatively conducted on the fine scale to give some suggestions on future urban planning, which is, hence, of great significance for urban function pattern analysis and urban planning.