Areas In High-resolution Images Research Articles

Lightweight Multilevel Feature-Fusion Network for Built-Up Area Mapping from Gaofen-2 Satellite Images

The timely, accurate acquisition of geographic spatial information such as the location, scope, and distribution of built-up areas is of great importance for urban planning, management, and decision-making. Due to the diversity of target features and the complexity of spatial layouts, the large-scale mapping of urban built-up areas using high-resolution (HR) satellite imagery still faces considerable challenges. To address this issue, this study adopted a block-based processing strategy and constructed a lightweight multilevel feature-fusion (FF) convolutional neural network for the feature representation and discrimination of built-up areas in HR images. The proposed network consists of three feature extraction modules composed of lightweight convolutions to extract features at different levels, which are further fused sequentially through two attention-based FF modules. Furthermore, to improve the problem of incorrect discrimination and severe jagged boundaries caused by block-based processing, a majority voting method based on a grid offset is adopted to achieve a refined extraction of built-up areas. The effectiveness of this method is evaluated using Gaofen-2 satellite image data covering Shenzhen, China. Compared with several state-of-the-art algorithms for detecting built-up areas, the proposed method achieves a higher detection accuracy and preserves better shape integrity and boundary smoothness in the extracted results.

Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs

Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision tasks, there are few publicly available SAR image datasets due to the high costs associated with acquisition and labeling. Recent works have applied deep learning methods for image translation between SAR and EO. However, the effectiveness of those techniques on high-resolution images has been hindered by a common limitation. Non-linear geometric distortions, induced by different imaging principles of optical and radar sensors, have caused insufficient pixel-wise correspondence between an EO-SAR patch pair. Such a phenomenon is not prominent in low-resolution EO-SAR datasets, e.g., SEN1-2, one of the most frequently used datasets, and thus has been seldom discussed. To address this issue, a new dataset SN6-SAROPT with sub-meter resolution is introduced, and a novel image translation algorithm designed to tackle geometric distortions adaptively is proposed in this paper. Extensive experiments have been conducted to evaluate the proposed algorithm, and the results have validated its superiority over other methods for both SAR to EO (S2E) and EO to SAR (E2S) tasks, especially for urban areas in high-resolution images.

Automatic newly increased built-up area extraction from high-resolution remote sensing images using line-density-based visual saliency and PanTex

Feature-based change detection technologies using multitemporal remote sensing images are widely applied to find newly increased built-up areas (NIBUA) during the period of observation. This paper proposes an automatic object-based NIBUA extraction method using high-resolution remote sensing images, which is based on the integration of spectrum feature, edge-derived line-density-based visual saliency (LDVS) feature, and texture-derived built-up presence index (PanTex) feature. In the proposed method, image segmentation is first employed to obtain objects as basic units of detection. Next, due to the complexity of built-up areas in high-resolution images, LDVS images and PanTex images are produced for each temporal image, respectively. Then, to highlight built-up areas in complex scenes, a comprehensive measure for each object is calculated by integrating the newly increased measures from spectrum, LDVS, and PanTex features via a manner of Dempster–Shafer evidence fusion. Finally, the object-based NIBUA can be extracted by conducting binarization on the newly increased fused measure image. Comparison studies and experimental results demonstrate that our method can achieve a robust extraction of NIBUA from high-resolution remote sensing images with a higher detection accuracy. We conclude that this automatic way can play a positive role in reducing the artificial workload of the interpreters and the cost of monitoring a large-region area. It is encouraged to employ this method in a variety of applications, such as illegal construction land monitoring, land use/cover map update, and city planning.

BASI: a new index to extract built-up areas from high-resolution remote sensing images by visual attention model

A built-up areas saliency index (BASI) to extract built-up areas is proposed in this article. The proposed method is designed to be especially effective for dealing with high-resolution remote sensing images and complex scenes. Due to the complexity of built-up areas in high-resolution images, visual attention model based on textural feature is applied for the calculation of BASI. To highlight built-up areas in complex scenes, we present an improved signal processing method to describe the textural feature of built-up areas, which is used as the low-layer feature of the visual attention model. Comparison studies and experimental results demonstrate the accuracy and robustness of BASI for discrimination between built-up and non-built-up areas from satellite and aerial high-resolution remote sensing data.