{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T09:01:02Z","timestamp":1764061262997,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation for Young Scientists of China","award":["62201059","JCKY2021602B037","2023YCXY006"],"award-info":[{"award-number":["62201059","JCKY2021602B037","2023YCXY006"]}]},{"name":"Foundation","award":["62201059","JCKY2021602B037","2023YCXY006"],"award-info":[{"award-number":["62201059","JCKY2021602B037","2023YCXY006"]}]},{"name":"BIT Research and Innovation Promoting Project","award":["62201059","JCKY2021602B037","2023YCXY006"],"award-info":[{"award-number":["62201059","JCKY2021602B037","2023YCXY006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Performing remote sensing scene classification (RSSC) directly on satellites can alleviate data downlink burdens and reduce latency. Compared to convolutional neural networks (CNNs), the all-adder neural network (A2NN) is a novel basic neural network that is more suitable for onboard RSSC, enabling lower computational overhead by eliminating multiplication operations in convolutional layers. However, the extensive floating-point data and operations in A2NNs still lead to significant storage overhead and power consumption during hardware deployment. In this article, a shared scaling factor-based de-biasing quantization (SSDQ) method tailored for the quantization of A2NNs is proposed to address this issue, including a powers-of-two (POT)-based shared scaling factor quantization scheme and a multi-dimensional de-biasing (MDD) quantization strategy. Specifically, the POT-based shared scaling factor quantization scheme converts the adder filters in A2NNs to quantized adder filters with hardware-friendly integer input activations, weights, and operations. Thus, quantized A2NNs (Q-A2NNs) composed of quantized adder filters have lower computational and memory overheads than A2NNs, increasing their utility in hardware deployment. Although low-bit-width Q-A2NNs exhibit significantly reduced RSSC accuracy compared to A2NNs, this issue can be alleviated by employing the proposed MDD quantization strategy, which combines a weight-debiasing (WD) strategy, which reduces performance degradation due to deviations in the quantized weights, with a feature-debiasing (FD) strategy, which enhances the classification performance of Q-A2NNs through minimizing deviations among the output features of each layer. Extensive experiments and analyses demonstrate that the proposed SSDQ method can efficiently quantize A2NNs to obtain Q-A2NNs with low computational and memory overheads while maintaining comparable performance to A2NNs, thus having high potential for onboard RSSC.<\/jats:p>","DOI":"10.3390\/rs16132403","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T08:17:29Z","timestamp":1719821849000},"page":"2403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4717-2304","authenticated-orcid":false,"given":"Ning","family":"Zhang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"He","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Jue","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0671-6777","authenticated-orcid":false,"given":"Guoqing","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Wenchao","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"ref_1","first-page":"5517010","article-title":"Convolutional neural networks for multimodal remote sensing data classification","volume":"60","author":"Wu","year":"2021","journal-title":"IEEE Trans. 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