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

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators

International audienceNonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Decision-based fusion of pansharpened VHR satellite images using two-level rolling self-guidance filtering and edge information

Pan-sharpening (PS) fuses low-resolution multispectral (LR MS) images with high-resolution panchromatic (HR PAN) bands to produce HR MS data. Current PS methods either better maintain the spectral information of MS images, or better transfer the PAN spatial details to the MS bands. In this study, we propose a decision-based fusion method that integrates two basic pan-sharpened very-high-resolution (VHR) satellite imageries taking advantage of both images simultaneously. It uses two-level rolling self-guidance filtering (RSGF) and Canny edge detection. The method is tested on Worldview (WV)-2 and WV-4 VHR satellite images on the San Fransisco and New York areas, using four PS algorithms. Results indicate that the proposed method increased the overall spectral-spatial quality of the base pan-sharpened images by 7.2% and 9.8% for the San Fransisco and New York areas, respectively. Our method therefore effectively addresses decision-level fusion of different base pan-sharpened images