Hangzhou, China / May 26, 2020 Recently, Dahua Technology’s AI-based Remote Sensing Image Analysis Technology has obtained first place in the comprehensive precision ranking of the Onera Satellite Change Detection (OSCD) Evaluation released by Geoscience and Remote Sensing Society (GRSS). This achievement fully demonstrates Dahua Technology’s continuous development and innovation capabilities in the field of remote sensing image change detection.
Dahua Technology (DH_RSIA) Ranked 1st in the Onera Satellite Change Detection Evaluation
OSCD (Onera Satellite Change Detection) is jointly issued and maintained by the International Institute of Electronics and Electrical Engineers (IEEE), and the Image Analysis and Data Fusion Technical Committee (IADF TC). It is an international authoritative evaluation platform for remote sensing image change detection algorithm. This evaluation involves complex and variable global surface coverage data, which is extremely challenging and attracts scholars and well-known academic institutions across the globe to participate.
In view of the large size of remote sensing images and the imbalance in the types of changing areas, Dahua Technology has proposed a method of image stretching and normalization preprocessing based on multi-channel fusion in data processing, which significantly solves issues including obvious surface differences. In terms of model structure, the innovative use of the Tversky loss function optimizes the problem of category imbalance. At the same time, Dahua Technology innovatively builds multi-modality and greatly improves the precision and recall of its algorithm. The Dahua Remote Sensing Image Analysis Technology has set another evaluation record in the remote sensing image change detection data set, achieving first place in the overall ranking.
Algorithm Framework of Remote Sensing Image Change Detection
Remote Sensing Image Change Detection
Based on the change detection algorithm of remote sensing images, the Remote Sensing Image Change Detection Technology uses remote sensing images of different phases to obtain the dynamic change information of the land cover type in the specified area, and assigns semantic category labels to image pixels that change with time, which is widely used in ecological resources monitoring, urban construction
management and other fields.
City-level Remote Sensing Image Change Detection Effect
In the field of ecological resources monitoring, the remote sensing image change detection algorithm can eliminate interference factors such as season and weather by comparing the remote sensing images of the same area before and after (two time phases) to obtain the spatio-temporal changes in the ecological geology of a wide area. It can be applied to acquire coverage information including periodic monitoring of water bodies, vegetation, minerals, etc., providing a scientific basis for scenarios such as resource development, environmental pollution, and natural disaster assessment.
From left to right: remote sensing images of different phases, marked valuesof
the changed areas, and the output results of the algorithm.
From top to bottom: buildings, water bodies and geological monitoring
In the field of urban construction management, the Remote Sensing Image Semantic Segmentation Technology can be used to automatically obtain the location, range, type and other information of the area where the nature of the land changes, achieving a city-level intelligent inspection of illegal buildings. At the same time, the Remote Sensing Image Object Detection Technology can be used to effectively extract distribution information of urban infrastructures such as sports venues, dynamically monitors the construction process of infrastructure facilities within the city, and provides effective data support for urban infrastructure auditing. In addition, the combination of high-altitude and ground monitoring data can achieve the integration of ground, air and sky monitoring coverage without dead angle, providing a comprehensive and high-precision spatial visualization for urban construction management.