Advances in Object-Based Image Analysis—Linked with Computer Vision and Machine Learning

Dear Colleagues,

Remote sensing and Earth observations from diverse sources, including satellite, airborne, in situ platforms and citizen observatories offer great opportunities to identify the characteristics and changes on the Earth’s surface across different scales. Recent development on unmanned aerial vehicles (UAV) further opens up an era for new applications, such as surveillance, precision farming, disaster relief and urban planning and management. Traditional remote sensing techniques are unsuitable to process the massive data captured and ineffective to extract meaningful information from highly complex and heterogeneous remote sensing datasets. This calls for powerful technologies to mine the robust and accurate information in an automatic fashion.

Object-based image analysis (OBIA) provides an excellent tool to incorporate process and feature knowledge, in addition to providing an effective way of dealing with information extraction at multiple scales. The object-based approach has undergone a step-by-step evolution, comprising the development of new segmentation methods, the integration of new classification methods and the development of new methods for change detection and monitoring. Deep Learning (DL), as the state-of-the-art breakthrough in AI and computer vision, offers a different outlook on feature learning and representations, where the most robust and representative features are learnt end-to-end, hierarchically. The combination of OBIA and DL represents an exciting area of research and has the potential to boost the precision of many practical applications to ground-breaking performances. In this Special Issue, we welcome submissions that offer the most recent advancements in deep learning and object-based image analysis for processing and analysing remotely sensed imagery. The topics of interest include but are not limited to the following:

• Semantic segmentation;
• Land cover and land use classification;
• Change detection;
• Deep convolutional neural networks (CNN) and other classification techniques;
• Object-based change detection and monitoring methods;
• Deep learning for data integration and sensor fusion;
• Cloud computing and Big Earth Data in deep learning and OBIA;
• Applications of deep learning and OBIA in remote sensing.

Dr. Ce Zhang
Prof. Peter M. Atkinson
Dr. Huapeng Li
Guest Editors

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