Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

Abstract

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.

High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction

“…However, the cultivated land, as a man-made concept, usually shows different spectral characteristics due to the varying types of crops, different irrigation methods, and different soil types, as well as fallow land plots. As a result, for classification, the intra-class variation increases and the inter-class separability decreases [6,7]. The frequently used traditional pixel-based classifiers, such as support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF) [8,9], and the object-based farmland extraction models, such as the stratified object-based farmland extraction [6], the superpixels and supervised machine-learning model [10], and the time-series-based methods [11], usually require the prior knowledge to model the high intra-class variation of the spatial or spectral features.…”

DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data

“…Based on the above reasons, we propose a stratified scale estimation strategy, which combines the area division based on the normalized grey level co-occurrence matrix (NGLCM) with the spatial scale estimation to obtain the segmentation object. As shown in Figure 2, first, the entire image is stratified into several large regions by multi-texture computing, and then the spatial scale of each region is estimated to implement fine-scale segmentation [52,55,56]. To some extent, the strategy can avoid the blindness and subjectivity of scale parameter selection, can satisfy the suitability and accuracy of different geographic objects, and can improve the efficiency of experiments.…”

Machine Learning Paradigm towards Content Based Image Retrieval on High Resolution Satellite Images

“…Lu Xu et al [21] presented a stratified object-primarily based farmland extraction method. It includes two key procedures: one is image region division on a scale and the alternative is scale parameter pre-estimation inside the local regions.…”