PE&RS January 2022 Issue Peer-Reviewed Papers

Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

Qing Ding, Zhenfeng Shao, Xiao Huang, Orhan Altan, and Yewen Fan

Taking the Futian District as the research area, this article proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated.

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

Clement E. Akumu and Eze O. Amadi

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this article was to examine the integration of Landsat Operational Land Imager (OLI) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data.

Augmented Sample-Based Real-Time Spatiotemporal Spectral Unmixing

Xinyu Ding and Qunming Wang

Recently, the method of spatiotemporal spectral unmixing (STSU) was developed to fully explore multi-scale temporal information (e.g., MODIS–Landsat image pairs) for spectral unmixing of coarse time series (e.g., MODIS data). To further enhance the application for timely monitoring, the real-time STSU (RSTSU) method was developed for real-time data. In this article, to extract more reliable training samples, we propose choosing the auxiliary MODIS–Landsat data temporally closest to the prediction time. To deal with the cloud contamination in the auxiliary data, we propose an augmented sample-based RSTSU (ARSTSU) method.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

Muhammad Nasar Ahmad, Zhenfeng Shao, and Orhan Altan

This article comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively.

Remote Sensing and Human Factors Research: A Review

Raechel A. Portelli and Paul Pope

Human experts are integral to the success of computational earth observation. They perform various visual decision-making tasks, from selecting data and training machine learning algorithms to interpret accuracy and credibility. Research concerning the various human factors which affect performance has a long history within the fields of earth observation and the military. Shifts in the analytical environment from analog to digital workspaces necessitate continued research, focusing on human-in-the-loop processing. This article reviews the history of human-factors research within the field of remote sensing and suggests a framework for refocusing the discipline’s efforts to understand the role that humans play in earth observation.

Multi-View Urban Scene Classification with a Complementary-Information Learning Model

Wanxuan Geng, Weixun Zhou, and Shuanggen Jin

Traditional urban scene classification approaches focus on images taken either by satellite or in aerial view. Although single-view images are able to achieve satisfactory results for scene classification in most situations, the complementary information provided by other image views is needed to further improve performance. Therefore, we present a complementary information-learning model (CILM) to perform multi-view scene classification of aerial and ground-level images. Specifically, the proposed CILM takes aerial and ground-level image pairs as input to learn view-specific features for later fusion to integrate the complementary information.