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Neural Network

Three-branch neural network for No-Reference Quality assessment of Pan-Sharpened Images


Pan-Sharpening (PS) techniques aim to enhance the spatial resolution of low-resolution multispectral images by leveraging data from high-resolution panchromatic images. Their comparison typically relies on the quality assessment of the resulting Full-Resolution (FS) pan-sharpened images. However, in the absence of a reference image, a dedicated No-Reference (NR) method must be employed. Therefore, this paper introduces a novel approach called the Three-Branch Neural Network for No-Reference Quality Assessment of Pan-Sharpened Images (TBN-PSI).

The network consists of three subnetworks designed for perceptual processing of image channels, featuring shared extraction of low-level features and high-level semantics. Extensive experimental evaluation demonstrates the superiority of the approach over the state-of-the-art NR PS image quality assessment methods, using six datasets containing diverse satellite images that span urban areas, green vegetation, and water scenarios. Specifically, TBN-PSI outperforms the compared methods by 4% to 9% in terms of Spearman’s Rank-Order Correlation Coefficient (SRCC), Pearson’s Linear Correlation Coefficient (PLCC), and Kendall’s Rank Correlation Coefficient (KRCC) between the obtained scores and those of three representative full-reference methods.

Architecture

The proposed TBN-PSI uses a predefined number of blocks of layers from the deep learning backbone network to create three connected subnetworks. Specifically, the proposed architecture is based on the first 87 layers of the 315 layers in the Inception-v3 network. The set of useful layers, gathered into blocks, has been determined experimentally, taking into account the image quality assessment perspective (see ablation tests in Section 4). Sets of blocks have been duplicated to handle two.
The proposed approach is evaluated on the NBU PansharpRSData benchmark (Meng et al., 2021), which consists of six satellite datasets (see Fig. 2): IKONOS (Agudelo-Medina et al., 2019), Quickbird (Toutin and Cheng, 2002), Gaofen (Chen et al., 2022), WorldView-4 (WV4) (Sefercik et al., 2021), WorldView-3 (WV3) (Longbotham et al., 2015), and WorldView-2 (WV2) (Padwick et al., 2010). The distinct features of each satellite dataset included in the NBU PansharpRSData.
In this study, a new low-complexity NR IQA method for predicting the quality of pan-sharpened images was introduced. The method employs a three-branch neural network architecture specifically designed for assessing the quality of pan-sharpened images. Selected blocks of layers from a backbone network were utilized, with two branches processing the input images—one dedicated to the RGB channels and the other to the NIR GB channels. A central branch integrates information from both inputs.

network, data transmission, cloud networking, 5G connectivity, cybersecurity, wireless technology, IoT integration, SDN, communication efficiency, network infrastructure, scalability, automation, artificial intelligence, machine learning, reduced latency, data security, real-time analytics, hybrid networks, edge computing, VPNs

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