Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

Abstract

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

“…To help judge the results of the change detection, five objective indicators were used as measures, namely, false positives (FPs) [50,62], false negatives (FNs) [50,63], overall errors (OEs) [50,64], percentage correct classification (PCC) [50,65], and kappa coefficient (KC) [50,66]. In the binary ground-truth image, we calculated the actual number of pixels belonging to the unchanged class (Nu) and the changed class (Nc).…”

Geoparcel-Based Spatial Prediction Method for Grassland Fractional Vegetation Cover Mapping

“…In such an HSR-RS image, the boundary of the grassland parcel is highlighted by merging a group of adjacent pixels. In addition, visual features, including spectrum, shape features and texture, were extracted from the HSR-RS image [24][25][26][27].…”

Integrating Backdating and Transfer Learning in an Object-Based Framework for High Resolution Image Classification and Change Analysis

“…For example, change analysis using high resolution images is still frequently based on traditional post-classification comparisons [14,15], which is time-consuming and may contain lots of "false changes" due to the propagation of errors from classification maps to the change analysis [16,17]. In the absence of long-term images (such as the Landsat satellite images) and global land cover products (such as the GlobeLand30), few studies have tested whether such advances in classification and change analysis can be applied to high spatial resolution imagery [9,10,18]. In particular, can a combination of such advances significantly improve accuracy and efficiency when using high spatial resolution imagery?…”