Reproducibility of Pansharpening Methods and Quality Indexes versus Data Formats

In this work, we investigate whether the performance of pansharpening methods depends on their input data format; in the case of spectral radiance, either in its original floating-point format or in an integer-packed fixed-point format. It is theoretically proven and experimentally demonstrated that methods based on multiresolution analysis are unaffected by the data format. Conversely, the format is crucial for methods based on component substitution, unless the intensity component is calculated by means of a multivariate linear regression between the upsampled bands and the lowpass-filtered Pan. Another concern related to data formats is whether quality measurements, carried out by means of normalized indexes depend on the format of the data on which they are calculated. We will focus on some of the most widely used with-reference indexes to provide a novel insight into their behaviors. Both theoretical analyses and computer simulations, carried out on GeoEye-1 and WorldView-2 datasets with the products of nine pansharpening methods, show that their performance does not depend on the data format for purely radiometric indexes, while it significantly depends on the data format, either floating-point or fixed-point, for a purely spectral index, like the spectral angle mapper. The dependence on the data format is weak for indexes that balance the spectral and radiometric similarity, like the family of indexes, Q2n, based on hypercomplex algebra.

論《龍龕手鑑》正俗體字聲符替換與唐五代西北方音

<p>遼僧行均所著《龍龕手鑑》，成書於宋太宗至道三年（997），乃專為佛教徒通解文字研讀佛典所編纂的一部字書。當中引用他書的情況相當複雜，除了佛經音義書，還有不少前代字書、韻書，因此不管是在音韻性質或者語料時代的判別上，皆不宜做太單純的思考。本文在孔仲溫、儲泰松等學者的啟發下，一方面考察當中眾多參用資料的成書年代，以確定其語料的時代性，繼而觀察筆者從中整理所得的四百多筆正俗體字聲符替換字組，並分析存在其間的音韻現象。發現當中某些音韻表現與唐五代西北方音的音韻特徵具有一致性，與過去學者們運用反切系聯法，以系聯當中複雜音切所得結果有所不同。在跳脫《切韻》或是通語雅音的音系框架之外，得以看見《龍龕手鑑》不同面向的音韻內涵。</p> <p>&nbsp;</p><p>Longkan Shoujian, a dictionary completed by a Liao dynasty monk named Xingjun in 997, is used to aid Buddhists in the study of Buddhist text and scriptures. Because Longkan Shoujian cites from numerous sources including yinyishu (dictionaries of pronunciations and meanings), dictionaries published in previous dynasties, and rhyme dictionaries, one must investigate thoroughly when attempting to decipher the phonological characteristics of Longkan Shoujian and the dynasties in which its corpuses were written. Inspired by scholars such as Chung-wen Kung and Tai-song Chu, this study examined the years in which many of the cited materials were completed to verify the dynasties in which the corpuses of Longkan Shoujian were written. Subsequently, this study explored the phonetic component substitution groups for the 400+ canonical and noncanonical Chinese characters that it had organized and summarized; and analyzed the phonological phenomena within. The study results showed that some of the phonological characteristics were similar to those of Northwestern China in the Five Dynasties period, challenging the results obtained by previous scholars using the fanqie association method (which involves separating a character&rsquo;s pronunciation into two other characters). By not accepting information preached in Qieyun and the phonological system of common languages in China as the gospel truth, this study discovered the different phonological characteristics observed in Longkan Shoujian.</p> <p>&nbsp;</p>

Reconfiguring Aviation for a Climate-Safe Future: Are Airlines Sending the Wrong Message?

Aviation remains a problematic sector of the global economy in times of climate emergency. Grounded in the ideology of reconfiguration, we adopt a system transitions perspective to address high emissions leisure travel. Our focus falls on the marketing communications of airlines as a critical component in the prevailing sociotechnical regime. Thematic analysis of the e-mail marketing communications of selected airlines revealed three prominent tropes: adventure and discovery; privilege; and urgency. These communications bring air travel into the everyday lives of consumers and accelerate the turnover time of tourist consumption. Time is mobilized to create a sense of resource scarcity and urgency to consume, paradoxically in a situation characterized by oversupply. The COVID-19 pandemic has presented a unique opportunity for structural reform of the airline industry. Component substitution to address airline marketing is required as an important step toward overcoming consumer moral disengagement and reconfiguring the airline industry.

PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts have used either component substitution (CS)-based methods or multiresolution analysis (MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose a pansharpening weight network (PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainable neural network (NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

Pansharpening by Complementing Compressed Sensing with Spectral Correction

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

COMPONENT SUBSTITUTION NETWORK FOR PAN-SHARPENING VIA SEMI-SUPERVISED LEARNING

Pan-sharpening refers to the technology which fuses a low resolution multispectral image (MS) and a high resolution panchromatic (PAN) image into a high resolution multispectral image (HRMS). In this paper, we propose a Component Substitution Network (CSN) for pan-sharpening. By adding a feature exchange module (FEM) to the widely used encoder-decoder framework, we design a network following the general procedure of the traditional component substitution (CS) approaches. Encoder of the network decomposes the input image into spectral feature and structure feature. The FEM regroups the extracted features and combines the spectral feature of the MS image with the structure feature of the PAN image. The decoder is an inverse process of the encoder and reconstructs the image. The MS and the PAN image share the same encoder and decoder, which makes the network robust to spectral and spatial variations. To reduce the burden of data preparation and improve the performance on full-resolution data, the network is trained through semi-supervised learning with image patches at both reduced-resolution and full-resolution. Experiments performed on GeoEye-1 data verifies that the proposed network has achieved state-of-the-art performance, and the semi-supervised learning stategy further improves the performance on full-resolution data.

Improving Component Substitution Pan-Sharpening Through Refinement of the Injection Detail

This article presents a novel strategy for improving the well-established component substitution-based multispectral image fusion methods, because the fused results obtained by component substitution methods tend to exhibit significant spectral distortion. The main cause of spectral distortion is analyzed and discussed based on the component substitution method's general model. An improved scheme is derived from the sensitivity imaging model to refine the approximate spatial detail and obtain one that is almost ideal. The experimental results on two data sets show that when it has been integrated into the Gram–Schmidt method and the generalized intensity-hue-saturation method, the proposed scheme allows the production of fused images of the same spatial sharpness as standard implementations but with significantly increased spectral quality. Quantitative scores and visual inspection at full resolution and spatially reduced resolution confirm the superiority of the improved methods over the conventional algorithms.