Pansharpening with a decision fusion based on the local size information

International audiencePansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pansharpening methods produce images with different characteristics. In the 2006 IEEE Data Fusion Contest, A\' -trous Wavelet Transform based pansharpening (AWLP) and Context Adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, it was observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to develop a method which could produce results locally approximately similar to the best method, among the two pansharpening methods. In this paper we propose a method which selects either of the two methods for performing pansharpening on local regions, based upon the size of the objects. The results obtained demonstrate that the proposed method produces images with quantitative results approximately similar to the method which is better among the AWLP and CBD pansharpening methods

An innovative structural health assessment tool for existing precast concrete buildings using deep learning methods and thermal infrared satellite imagery

Currently, there is a limited number of tools that can be used to assess progressive damage of buildings in large-scale study areas. The effectiveness of such tools is also constrained by a lack of sufficient and reliable data from the buildings and the area itself. This research article presents an innovative framework for damage detection and classification of precast concrete (PC) buildings based on satellite infrared (IR) imagery. The framework uses heat leakage changes over time to assess the progressive damage of buildings. Multispectral satellite images are used for a spatial scanning and large-scale assessment of a study area. A deep learning object detection algorithm coupled with two pixel intensities classification approaches are utilized in the framework. The proposed framework is demonstrated on two case study areas (parts of Karaganda and Almaty cities) in Kazakhstan using a set of multitemporal satellite images. Overall, the proposed framework, in combination with a YOLOv3 algorithm, successfully detects 85% of the PC buildings in the study areas. The use of a peak heat leakage classification approach (in comparison to mean heat leakage classification) over the 4 years showed a good agreement with the proposed framework. On-site visual inspections confirmed that PC buildings that were classified as having “High damage probability” have indeed evident signs of deterioration, as well as a more heat leakage than the rest of the buildings in the study areas. Whilst the framework has some limitations such as its applicability to extreme continental climate and its low sensitivity to detect minor damage, the proposed innovative framework showed very promising results at detecting progressive damage in PC buildings. This article contributes towards developing more efficient long-term damage assessment tools for existing buildings in large urban areas