{"title":"基于空间背景的高光谱解混与基于注意机制的端元集成学习","authors":"R.M.K.L. Ratnayake, D.M.U.P. Sumanasekara, H.M.K.D. Wickramathilaka, G.M.R.I. Godaliyadda, H.M.V.R. Herath, M.P.B. Ekanayake","doi":"10.1016/j.ophoto.2025.100086","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. Most of these networks use an Endmember Extraction Algorithm(EEA) for the initialization of their network. As EEAs performance depends on the environment, single initialization does not ensure optimum performance. Also, only a few networks utilize the spatial context in HS Images to solve the unmixing problem. In this paper, we propose Hyperspectral Unmixing with Spatial Context and Endmember Ensemble Learning with Attention Mechanism (SCEELA) to address these issues. The proposed method has three main components, Signature Predictor (SP), Pixel Contextualizer (PC) and Abundance Predictor (AP). SP uses an ensemble of EEAs for each endmember as the initialization and the attention mechanism within the transformer enables ensemble learning to predict accurate endmembers. The attention mechanism in the PC enables the network to capture the contextual data and provide a more refined pixel to the AP to predict the abundance of that pixel. SCEELA was compared with eight state-of-the-art HS unmixing algorithms for three widely used real datasets and one synthetic dataset. The results show that the proposed method shows impressive performance when compared with other state-of-the-art algorithms.</div></div>","PeriodicalId":100730,"journal":{"name":"ISPRS Open Journal of Photogrammetry and Remote Sensing","volume":"15 ","pages":"Article 100086"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperspectral unmixing with spatial context and endmember ensemble learning with attention mechanism\",\"authors\":\"R.M.K.L. Ratnayake, D.M.U.P. Sumanasekara, H.M.K.D. Wickramathilaka, G.M.R.I. Godaliyadda, H.M.V.R. Herath, M.P.B. Ekanayake\",\"doi\":\"10.1016/j.ophoto.2025.100086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. Most of these networks use an Endmember Extraction Algorithm(EEA) for the initialization of their network. As EEAs performance depends on the environment, single initialization does not ensure optimum performance. Also, only a few networks utilize the spatial context in HS Images to solve the unmixing problem. In this paper, we propose Hyperspectral Unmixing with Spatial Context and Endmember Ensemble Learning with Attention Mechanism (SCEELA) to address these issues. The proposed method has three main components, Signature Predictor (SP), Pixel Contextualizer (PC) and Abundance Predictor (AP). SP uses an ensemble of EEAs for each endmember as the initialization and the attention mechanism within the transformer enables ensemble learning to predict accurate endmembers. The attention mechanism in the PC enables the network to capture the contextual data and provide a more refined pixel to the AP to predict the abundance of that pixel. SCEELA was compared with eight state-of-the-art HS unmixing algorithms for three widely used real datasets and one synthetic dataset. The results show that the proposed method shows impressive performance when compared with other state-of-the-art algorithms.</div></div>\",\"PeriodicalId\":100730,\"journal\":{\"name\":\"ISPRS Open Journal of Photogrammetry and Remote Sensing\",\"volume\":\"15 \",\"pages\":\"Article 100086\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS Open Journal of Photogrammetry and Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667393225000055\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Open Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667393225000055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
近年来,基于变压器的深度学习网络由于其优越的性能在高光谱(HS)解混应用中得到了广泛的应用。这些网络大多使用端点提取算法(end - member Extraction Algorithm, EEA)来初始化它们的网络。由于EEAs的性能取决于环境,因此单个初始化并不能确保最佳性能。此外,只有少数网络利用HS图像中的空间上下文来解决解混问题。本文提出了基于空间上下文的高光谱解混和基于注意机制的端元集成学习(SCEELA)来解决这些问题。该方法由三个主要部分组成:特征预测器(SP)、像素上下文预测器(PC)和丰度预测器(AP)。SP对每个端成员使用eea集合作为初始化,转换器内的注意机制使集成学习能够预测准确的端成员。PC中的注意力机制使网络能够捕获上下文数据,并向AP提供更精细的像素,以预测该像素的丰度。在3个广泛使用的真实数据集和1个合成数据集上,对SCEELA与8种最先进的HS解混算法进行了比较。结果表明,与其他先进算法相比,该方法具有令人印象深刻的性能。
Hyperspectral unmixing with spatial context and endmember ensemble learning with attention mechanism
In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. Most of these networks use an Endmember Extraction Algorithm(EEA) for the initialization of their network. As EEAs performance depends on the environment, single initialization does not ensure optimum performance. Also, only a few networks utilize the spatial context in HS Images to solve the unmixing problem. In this paper, we propose Hyperspectral Unmixing with Spatial Context and Endmember Ensemble Learning with Attention Mechanism (SCEELA) to address these issues. The proposed method has three main components, Signature Predictor (SP), Pixel Contextualizer (PC) and Abundance Predictor (AP). SP uses an ensemble of EEAs for each endmember as the initialization and the attention mechanism within the transformer enables ensemble learning to predict accurate endmembers. The attention mechanism in the PC enables the network to capture the contextual data and provide a more refined pixel to the AP to predict the abundance of that pixel. SCEELA was compared with eight state-of-the-art HS unmixing algorithms for three widely used real datasets and one synthetic dataset. The results show that the proposed method shows impressive performance when compared with other state-of-the-art algorithms.