面向遙感圖像解譯的增量深度學(xué)習(xí)

翁星星; 龐超; 許博文; 夏桂松

doi:10.11999/JEIT240172

面向遙感圖像解譯的增量深度學(xué)習(xí)

doi: 10.11999/JEIT240172

翁星星¹,
龐超¹,
許博文¹,
夏桂松^{1, 2, ,}

1.
武漢大學(xué)計(jì)算機(jī)學(xué)院武漢 430072
2.
武漢大學(xué)測繪遙感信息工程國家重點(diǎn)實(shí)驗(yàn)室武漢 430079

基金項(xiàng)目: 國家自然科學(xué)基金(U22B2011, 62325111)

詳細(xì)信息

作者簡介:
翁星星：女，博士生，研究方向?yàn)檫b感圖像智能解譯的增量學(xué)習(xí)

龐超：男，博士生，研究方向?yàn)檫b感圖像變化檢測、計(jì)算機(jī)視覺

許博文：女，博士生，研究方向?yàn)檫b感圖像智能解譯、計(jì)算機(jī)視覺

夏桂松：男，博士，教授，研究方向?yàn)橛?jì)算機(jī)視覺、遙感圖像解譯

通訊作者:
夏桂松　guisong.xia@whu.edu.cn

中圖分類號: TN911.73; TP751
計(jì)量
- 文章訪問數(shù): 1445
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-03-14
- 修回日期: 2024-05-14
- 網(wǎng)絡(luò)出版日期: 2024-05-23
- 刊出日期: 2024-10-30

Incremental Deep Learning for Remote Sensing Image Interpretation

1.
School of Computer Science, Wuhan University, Wuhan 430072, China
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds: The National Natural Science Foundation of China (U22B2011, 62325111)

摘要

摘要: 深度學(xué)習(xí)的發(fā)展推動了高精度遙感圖像智能解譯模型的涌現(xiàn)。然而，目前遙感智能解譯模型大多基于預(yù)先定義的靜態(tài)數(shù)據(jù)集獨(dú)立訓(xùn)練，難以適應(yīng)環(huán)境開放和需求動態(tài)的實(shí)際應(yīng)用，嚴(yán)重阻礙了遙感智能解譯模型的廣域和長期運(yùn)用。增量學(xué)習(xí)能使模型持續(xù)學(xué)習(xí)新知識，并保持對舊知識的記憶，近年來，被廣泛應(yīng)用于推動遙感智能解譯模型演化、提升模型智能解譯性能。該文面向多模態(tài)遙感數(shù)據(jù)、不同類型解譯任務(wù)，全面調(diào)研了遙感圖像智能解譯增量學(xué)習(xí)方法，從遺忘問題解決思路、解譯模型進(jìn)化應(yīng)用兩個(gè)層面梳理了現(xiàn)有研究工作。在此基礎(chǔ)上，從促進(jìn)遙感圖像解譯模型進(jìn)化研究的角度，展望和討論了遙感領(lǐng)域增量學(xué)習(xí)的未來研究方向。
- 遙感圖像解譯 /
- 深度模型 /
- 增量學(xué)習(xí) /
- 模型進(jìn)化
Abstract: The significant advancement of deep learning has facilitated the emergence of high-precision interpretation models for remote-sensing images. However, a notable drawback is that the majority of interpretation models are trained independently on static datasets, rendering them incapable of adapting to open environments and dynamic demands. This limitation poses a substantial obstacle to the widespread and long-term application of remote-sensing interpretation models. Incremental learning, empowering models to continuously learn new knowledge while retaining previous knowledge, has been recently utilized to drive the evolution of interpretation models and improve their performance. A comprehensive investigation of incremental learning methods for multi-modal remote sensing data and diverse interpretation tasks is provided in this paper. Existing research efforts are organized and reviewed in terms of mitigating catastrophic forgetting and facilitating interpretation model evolution. Drawing from this research progress, this study deliberates on the future research directions for incremental learning in remote sensing, with the aim of advancing research in model evolution for remote sensing image interpretation.
- Remote sensing image interpretation /
- Deep models /
- Incremental learning /
- Model evolution

HTML全文

圖 1 不同類型的遙感圖像場景識別模型進(jìn)化^[23]

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圖 2 不同形式的網(wǎng)絡(luò)局部模塊擴(kuò)展

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圖 3 常用的代表性樣本選擇方法結(jié)果示意圖

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圖 4 基于提示的增量學(xué)習(xí)^[68]

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圖 5 天空地多源可見光圖像^[88]

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表 1 面向遙感圖像解譯的增量學(xué)習(xí)方法對比與總結(jié)

方法	核心思想	優(yōu)點(diǎn)	缺點(diǎn)	代表性成果
知識蒸餾	新舊模型在同一輸入圖像上的輸出保持一致	以損失函數(shù)的形式約束模型的參數(shù)更新，簡單有效且易于實(shí)施	舊模型保存，占用一定的存儲空間	逐任務(wù)知識蒸餾^[24]
				背景建模知識蒸餾^[25]
				空間-通道壓縮特征蒸餾^[26]
				結(jié)構(gòu)化知識蒸餾^[27,28]
				歷史信息引導(dǎo)特征蒸餾^[29]
網(wǎng)絡(luò)擴(kuò)展	增加獨(dú)立網(wǎng)絡(luò)參數(shù)學(xué)習(xí)新知識	直接凍結(jié)已有網(wǎng)絡(luò)即可有效保持舊知識	不斷擴(kuò)大的網(wǎng)絡(luò)規(guī)模增加計(jì)算和存儲成本	增量學(xué)習(xí)建模為提升過程^[30,31]
				特征編碼器深層結(jié)構(gòu)擴(kuò)展^[32]
				殘差模塊擴(kuò)展^[33,34]
				特征遷移模塊擴(kuò)展^[35]
記憶回放	保留少部分舊數(shù)據(jù)，幫助模型回憶舊知識	模型基于新舊數(shù)據(jù)優(yōu)化，能夠較好地感知新舊知識的邊界	舊數(shù)據(jù)保留增加存儲成本，且易產(chǎn)生過擬合	代表性樣本選擇^[36–38]
				預(yù)測偏差校正^[39–46]
				舊數(shù)據(jù)特征生成^[24,47]

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表 2 面向可見光圖像解譯的類別增量學(xué)習(xí)方法總結(jié)

解譯任務(wù)	文獻(xiàn)	貢獻(xiàn)	遺忘問題解決思路
解譯任務(wù)	文獻(xiàn)	貢獻(xiàn)	知識蒸餾	網(wǎng)絡(luò)擴(kuò)展	記憶回放
場景識別	[31]	類別增量學(xué)習(xí)建模為特征提升過程，動態(tài)擴(kuò)展模塊化分類網(wǎng)絡(luò)	$ \surd $	$ \surd $	$ \surd $
	[38]	動態(tài)混合的樣本選擇策略和基于異構(gòu)原型的學(xué)習(xí)框架，增加存儲樣本的信息量	$ \surd $	$ \times $	$ \surd $
	[64]	利用可學(xué)習(xí)提示解耦場景識別的知識，避免特定知識的相互干擾緩解遺忘問題	$ \times $	$ \surd $	$ \times $
	[65]	相互協(xié)作的瞬時(shí)網(wǎng)絡(luò)和保持網(wǎng)絡(luò)實(shí)現(xiàn)有效的舊知識召回和新知識積累	$ \surd $	$ \times $	$ \times $
	[37]	凸包構(gòu)造算法選取接近類邊界的樣本	$ \times $	$ \times $	$ \surd $
	[53]	根據(jù)新舊類別的相似性設(shè)計(jì)類別學(xué)習(xí)順序，提高新模型的新類別學(xué)習(xí)效率	$ \surd $	$ \times $	$ \surd $
	[47]	利用VAE生成多樣的舊類別特征，避免過擬合和存儲成本高的問題	$ \times $	$ \times $	$ \surd $
	[40]	平衡的舊數(shù)據(jù)集微調(diào)新模型的預(yù)測頭，緩解新舊類別不平衡導(dǎo)致的預(yù)測偏差	$ \surd $	$ \times $	$ \surd $
	[63]	擴(kuò)展預(yù)測頭學(xué)習(xí)新類別，并依據(jù)圖像特征與任務(wù)原型的相似性選擇預(yù)測頭	$ \surd $	$ \surd $	$ \times $
目標(biāo)檢測	[45]	基于熵的蓄水池抽樣策略和樣本抽樣加權(quán)緩解回放不平衡導(dǎo)致的預(yù)測偏差	$ \times $	$ \times $	$ \surd $
目標(biāo)檢測	[49]	在區(qū)域候選網(wǎng)絡(luò)和預(yù)測頭添加分支并遷移知識，實(shí)現(xiàn)新類別學(xué)習(xí)和舊知識保留	$ \surd $	$ \surd $	$ \times $
地物分類	[28]	跨圖像特征相關(guān)性蒸餾損失增強(qiáng)模型的新類別學(xué)習(xí)能力	$ \surd $	$ \times $	$ \times $
	[24]	像素級舊特征生成，應(yīng)對遺忘問題；逐任務(wù)知識蒸餾避免新類別向舊類別壓縮	$ \surd $	$ \times $	$ \surd $
	[26]	空間-通道維度的特征壓縮并遷移，降低特征空間知識蒸餾的計(jì)算成本；信息熵量化舊模型預(yù)測的準(zhǔn)確性，并僅使用高置信度像素預(yù)測維持舊知識	$ \surd $	$ \times $	$ \times $
	[29]	歷史信息引導(dǎo)模型關(guān)注前景(舊類別)區(qū)域的知識遷移；高置信度的舊模型預(yù)測與真實(shí)標(biāo)簽相結(jié)合，為新模型提供完整的類別監(jiān)督信息	$ \surd $	$ \times $	$ \times $
	[66]	多樣蒸餾損失促使模型關(guān)注易被遺忘的小目標(biāo)和目標(biāo)邊緣	$ \surd $	$ \times $	$ \times $
	[67]	依據(jù)類別實(shí)例數(shù)量計(jì)算每張圖像的重要性，確保存儲圖像的類別均衡	$ \surd $	$ \times $	$ \surd $

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表 3 面向可見光遙感圖像解譯的類別增量學(xué)習(xí)常用數(shù)據(jù)集

解譯任務(wù)	數(shù)據(jù)集	圖像數(shù)量	類別數(shù)量	類別增量學(xué)習(xí)方法
場景識別	NWPU-RESISC45^[70]	31500	45	[37,53,64]
	FGSCR-42^[71]	9320	42	[38]
	PatternNet^[72]	30400	38	[53]
	RSICB-256^[73]	28000	35	[31,65]
	Optimal-31^[74]	1860	31	[47,63]
	AID^[75]	10000	30	[31,64,65]
	CLRS^[76]	15000	25	[40]
	UC-Merced^[77]	2100	21	[31,47,63–65]
	SIRI-WHU^[78]	2400	12	[37]
目標(biāo)檢測	DIOR^[79]	23463	20	[45,49]
	DOTA^[80]	2806	15	[45,49]
	NWPU VHR-10^[81]	800	10	[45]
地物分類	iSAID^[82]	2806	15	[24,28,29]
	GCSS^[83]	948	8	[29]
	Deepglobe^[84]	1146	7	[24,26]
	Potsdam^[85]/Vaihingen^[86]	38/33	6	[24,26,28,66,67]
	Luxcarta^[67]	–	5	[67,69]

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表 4 面向可見光圖像解譯的類別增量學(xué)習(xí)方法性能對比

解譯任務(wù)	研究工作	評價(jià)指標(biāo)	數(shù)據(jù)集	增量訓(xùn)練次數(shù)	得分(%)	發(fā)布時(shí)間
場景識別	[31]	mACC	RSICB-256	9	91.10	TGRS’2024
			AID	6	86.75
			UC-Merced	3	94.29
	[38]	ACC	FGSCR-42	8	89.06	TAES’2024
	[64]	ACC	NWPU-RESISC45	9	72.90	GRSL’2023
			AID	6	81.10
			UC-Merced	3	92.33
	[65]	mACC	RSICB-256	9	82.63	IEEE/CVF’2022
			AID	6	88.93
			UC-Merced	3	89.52
	[37]	ACC	NWPU-RESISC45	7	93.47	TGRS’2022
	[37]	ACC	SIRI-WHU	7	98.13	TGRS’2022
	[53]	mACC	NWPU-RESISC45	9	49.42	JSTARS’2021
	[53]	mACC	PatternNet	6	62.31	JSTARS’2021
	[47]	ACC	Optimal-31	10	86.80	GRSL’2022
	[47]	ACC	UC-Merced	7	94.20	GRSL’2022
	[40]	ACC	CLRS	4	32.30	CIOP’2021
	[63]	ACC	Optimal-31	10	71.00	GRSL’2022
	[63]	ACC	UC-Merced	7	89.00	GRSL’2022
目標(biāo)檢測	[45]	mAP	DIOR	20	34.40	EAAI’2023
			DOTA	15	54.90
			NWPU VHR-10	10	73.60
	[49]	mAP	DIOR	2	68.45	TGRS’2022
	[49]	mAP	DOTA	2	65.20	TGRS’2022
地物分類	[28]	mIoU	iSAID	6	31.88	TGRS’2023
			Potsdam	5	74.44
			Vaihingen	5	62.54
	[24]	mIoU	iSAID	6	31.71	TGRS’2022
			Deepglobe	6	57.00
			Potsdam	2	77.70
			Vaihingen	3	74.60
	[26]	mIoU	Deepglobe	6	52.40	TGRS’2022
			Potsdam	3	76.30
			Vaihingen	3	74.10
	[29]	mIoU	iSAID	6	30.21	TGRS’2022
	[29]	mIoU	GCSS	5	62.53	TGRS’2022
	[66]	mIoU	Potsdam	2	75.92	TGRS’2022
	[66]	mIoU	Vaihingen	3	73.96	TGRS’2022
	[67]	F1	Luxcarta	3	68.09	JSTARS’2019
			Potsdam	3	84.25
			Vaihingen	3	87.44

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表 5 面向可見光圖像解譯的域增量學(xué)習(xí)方法總結(jié)

解譯任務(wù)	文獻(xiàn)	貢獻(xiàn)	遺忘問題解決思路
解譯任務(wù)	文獻(xiàn)	貢獻(xiàn)	知識蒸餾	網(wǎng)絡(luò)擴(kuò)展	記憶回放
場景識別	[87]	雙網(wǎng)絡(luò)知識協(xié)同學(xué)習(xí)策略增強(qiáng)場景識別模型的新知識學(xué)習(xí)和舊知識鞏固能力	$ \surd $	$ \times $	$ \times $
目標(biāo)檢測	[88]	為特征空間、輸出空間的知識蒸餾添加可學(xué)習(xí)權(quán)重，解決預(yù)測偏差問題	$ \surd $	$ \times $	$ \surd $
地物分類	[30]	域增量學(xué)習(xí)建模為提升過程，并利用自適應(yīng)學(xué)習(xí)率確定每個(gè)網(wǎng)絡(luò)的重要性	$ \times $	$ \surd $	$ \times $
地物分類	[50]	擴(kuò)展整個(gè)網(wǎng)絡(luò)，新網(wǎng)絡(luò)的學(xué)習(xí)目標(biāo)是彌補(bǔ)已有模型在新數(shù)據(jù)上的性能不足	$ \times $	$ \surd $	$ \times $
變化檢測	[34]	輸出空間和多層次特征空間的知識蒸餾保留舊知識；擴(kuò)展域殘差單位和解碼器，學(xué)習(xí)新知識	$ \surd $	$ \surd $	$ \times $

下載: 導(dǎo)出CSV

表 6 面向可見光圖像解譯的域增量學(xué)習(xí)方法性能對比

解譯任務(wù)	文獻(xiàn)	評價(jià)指標(biāo)	數(shù)據(jù)集	增量訓(xùn)練次數(shù)	得分(%)	發(fā)布時(shí)間
場景識別	[87]	ACC	NWPU-RESISC45^[70]	5	80.53	計(jì)算機(jī)應(yīng)用’2024
場景識別	[87]	ACC	AID^[75]	5	77.40	計(jì)算機(jī)應(yīng)用’2024
目標(biāo)檢測	[88]	mAP@0.5	FASDD_CD^[91]$ \to $FASDD_RS^[91]	2	49.47	JAG’2023
目標(biāo)檢測	[88]	mAP@0.5	FASDD_RS^[91]$ \to $FLAME^[92]	2	51.53	JAG’2023
地物分類	[30]	OA	DREAM-B$ ? $^[30]$ \to $xBD^[93]$ \to $Haiti-xBD^[30]	3	97.94 (僅新域)	ISPRS’2023
地物分類	[50]	IoU	DREAM-B^[50]	4	63.72	Remote Sens.’2020
變化檢測	[34]	$ {\varDelta }_{\mathrm{I}\mathrm{o}\mathrm{U}} $	SYSU-CD^[89]$ \to $CDD^[90]$ \to $PRCV^[34]	3	8.22	TGRS’2024
$ \to $：指示模型增量學(xué)習(xí)順序

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表 7 面向可見光圖像解譯的任務(wù)增量學(xué)習(xí)方法總結(jié)

解譯任務(wù)	文獻(xiàn)	貢獻(xiàn)	遺忘問題解決思路
解譯任務(wù)	文獻(xiàn)	貢獻(xiàn)	知識蒸餾	網(wǎng)絡(luò)擴(kuò)展	記憶回放
場景識別	[35]	特征遷移模塊學(xué)習(xí)相鄰任務(wù)間的特征分布映射，提升模型的新任務(wù)學(xué)習(xí)能力，并避免了存儲成本和推理時(shí)間的增加	$ \surd $	$ \surd $	$ \times $
地物分類	[33]	擴(kuò)展域殘差適應(yīng)模塊和解碼器，學(xué)習(xí)新任務(wù)；設(shè)計(jì)重疊類別的知識蒸餾，應(yīng)對不同任務(wù)的標(biāo)簽空間偏移	$ \surd $	$ \surd $	$ \times $
地物分類	[27]	約束新舊模型的低層特征逐像素表征一致，同時(shí)深層特征像素親和矩陣相似，以保留在舊任務(wù)數(shù)據(jù)上學(xué)習(xí)到的像素交互信息	$ \surd $	$ \surd $	$ \times $

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表 8 面向可見光圖像解譯的任務(wù)增量學(xué)習(xí)性能對比

解譯任務(wù)	文獻(xiàn)	評價(jià)指標(biāo)	數(shù)據(jù)集	增量訓(xùn)練次數(shù)	得分(%)	發(fā)布時(shí)間
場景識別	[35]	mACC	AID^[75]	10	86.74	TGRS’2022
			BigEarthNet^[94]	5	95.89
			EuroSAT^[95]	2	94.85
			EuroSAT^[95]$ \to $BigEarthNet^[94]$ \to $RS-C11^[96]$ \to $ RSSCN7^[97]$ \to $AID^[75]$ \to $SIRI-WHU^[78]$ \to $SAT-4^[98]	7	79.86
地物分類	[33]	$ {\varDelta }_{\mathrm{m}\mathrm{I}\mathrm{o}\mathrm{U}} $	GID^[99]$ \to $BDCI2020^[100]$ \to $Deepglobe^[84]$ \to $ LoveDA-Urban^[101]$ \to $LoveDA-Rural^[101]	5	–5.46	Remote Sens.’2023
	[27]	mIoU	Deepglobe^[84]$ \to $Potsdam^[85]$ \to $GCSS^[83]	3	66.27	TGRS’2021
	[27]	mIoU	Vaihingen^[86]$ \to $Potsdam^[85]	2	79.72	TGRS’2021
$ \to $：指示模型增量學(xué)習(xí)順序

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表 9 面向合成孔徑雷達(dá)圖像目標(biāo)識別的類別增量學(xué)習(xí)方法總結(jié)

文獻(xiàn)	貢獻(xiàn)	遺忘問題解決思路
文獻(xiàn)	貢獻(xiàn)	知識蒸餾	網(wǎng)絡(luò)擴(kuò)展	記憶回放
[102]	基于廣義最大覆蓋的樣本選擇，降低計(jì)算成本	$ \times $	$ \times $	$ \surd $
[56]	基于局部分布統(tǒng)計(jì)信息和全局分布密度信息選擇代表性樣本；評估測試樣本的預(yù)測可靠性，并由此管理增量數(shù)據(jù)	$ \times $	$ \times $	$ \surd $
[32]	特征編碼器深層結(jié)構(gòu)擴(kuò)展結(jié)合記憶回放、知識蒸餾，應(yīng)對遺忘問題	$ \surd $	$ \surd $	$ \surd $
[44]	訓(xùn)練樣本抽樣加權(quán)和記憶增強(qiáng)的權(quán)重對齊，解決新舊類別不平衡導(dǎo)致的預(yù)測偏差	$ \surd $	$ \times $	$ \surd $
[54]	Openmax算法幫助模型識別未知類別，此后利用記憶回放賦予模型持續(xù)學(xué)習(xí)未知類別的能力	$ \times $	$ \times $	$ \surd $
[39]	可分離學(xué)習(xí)策略緩解新舊類別不平衡導(dǎo)致的預(yù)測偏差	$ \surd $	$ \times $	$ \surd $
[41]	樣本抽樣加權(quán)，構(gòu)建類別均衡的訓(xùn)練批次，校正預(yù)測偏差	$ \surd $	$ \times $	$ \surd $
[43]	類別分離損失解決新舊類別相似產(chǎn)生的混淆問題；偏差校正層應(yīng)對新舊類別不平衡現(xiàn)象	$ \surd $	$ \times $	$ \surd $
[46]	類別的有效樣本數(shù)加權(quán)交叉熵?fù)p失，解決新舊類別不平衡導(dǎo)致的預(yù)測偏差	$ \times $	$ \times $	$ \surd $
[36]	基于局部幾何和統(tǒng)計(jì)信息的類邊界樣本選擇，并利用SMOTE方法重采樣，豐富舊類別樣本	$ \times $	$ \times $	$ \surd $
[55]	基于局部幾何和統(tǒng)計(jì)信息的類邊界樣本選擇	$ \times $	$ \times $	$ \surd $

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表 10 面向合成孔徑雷達(dá)圖像目標(biāo)識別的類別增量學(xué)習(xí)方法性能對比

文獻(xiàn)	網(wǎng)絡(luò)架構(gòu)	評價(jià)指標(biāo)	數(shù)據(jù)集	每類存儲量	增量訓(xùn)練次數(shù)	得分(%)	發(fā)布時(shí)間
[38]	ResNet-34	ACC	MSTAR	50	10	83.42	TAES’2024
[102]	Autoencoder	OA	MSTAR	50	8	92.54	TGRS’2023
[56]	A-ConvNets	–	MSTAR	–	–	–	TGRS’2023
[32]	ViT-B	ACC	MSTAR	20	8	74.65	Remote Sens.’2023
[44]	–	ACC	MSTAR$ + $OpenSARShip	200(11個(gè)類別)	12	93.87	GRSL’2023
[54]	CNN	OA	MSTAR	–	3	92.70	RadarConf’2023
[39]	DCFM-CNN	ACC	MSTAR	30	7	91.76	TGRS’2022
[39]	DCFM-CNN	ACC	OpenSARShip	30	3	–	TGRS’2022
[41]	ResNet-18	ACC	MSTAR$ + $OpenSARShip	200(12個(gè)類別)	12	93.87	JSTARS’2022
[43]	ResNet-18	ACC (top-5)	MSTAR	20	10	97.17	Appli. Sci.’2022
[46]	ResNet-18	ACC	OpenSARShip	–	3	51.15	IGARSS’2022
[36]	–	ACC	MSTAR	–	10	–	TGRS’2020
[55]	–	ACC	MSTAR	888(9個(gè)類別)	8	86.50	TGRS’2019
$ + $：組合不同數(shù)據(jù)集模擬增量學(xué)習(xí)階段

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表 11 面向高光譜圖像分類的增量學(xué)習(xí)方法總結(jié)

增量學(xué)習(xí)類型	文獻(xiàn)	貢獻(xiàn)	遺忘問題解決思路
增量學(xué)習(xí)類型	文獻(xiàn)	貢獻(xiàn)	知識蒸餾	網(wǎng)絡(luò)擴(kuò)展	記憶回放
類別增量學(xué)習(xí)	[42]	新舊類別平衡集優(yōu)化偏差校正層，校準(zhǔn)模型在新類別上的輸出	$ \surd $	$ \times $	$ \surd $
類別增量學(xué)習(xí)	[105]	基于新舊類別的特征向量均值，提出線性規(guī)劃方法更新分類器權(quán)重	$ \times $	$ \surd $	$ \times $
任務(wù)增量學(xué)習(xí)	[106]	基于度量學(xué)習(xí)的光譜-空間特征蒸餾和輸出蒸餾幫助模型維持舊知識	$ \surd $	$ \times $	$ \times $

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表 12 面向高光譜圖像分類的增量學(xué)習(xí)方法性能對比

增量學(xué)習(xí)類型	文獻(xiàn)	評價(jià)指標(biāo)	數(shù)據(jù)集	增量訓(xùn)練次數(shù)	得分(%)	發(fā)布時(shí)間
類別增量學(xué)習(xí)	[42]	ACC	PaviaU	3	85.76	Remote Sens.’2022
			Salinas	5	93.31
			Houston	4	86.54
	[105]	ACC	PaviaU	2	73.06	TCybernatics’2020
			Indian Pines	–	–
			Salinas	–	–
任務(wù)增量學(xué)習(xí)	[106]	mACC	PaviaU$ \to $Indian Pines $ \to $Salinas$ \to $Houston	4	72.84	TGRS’2022
$ \to $：指示模型增量學(xué)習(xí)順序

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表 13 面向高光譜圖像分類的增量學(xué)習(xí)常用數(shù)據(jù)集

數(shù)據(jù)集	PaviaU^[107]	Salinas^[108]	Houston^[109]	Indian Pines^[110]
采集地點(diǎn)	意大利北部	加利福尼亞州	德克薩斯州	印第安納州
采集設(shè)備	ROSIS	AVIRIS	-	AVIRIS
光譜覆蓋范圍(μm)	0.43～0.86	0.4～0.25	0.38～1.05	0.4～2.5
空間分辨率(m)	1.3	3.7	2.5	20
波段數(shù)量	115	224	144	220
像素?cái)?shù)量	42776	54129	15029	21025
類別數(shù)量	9	16	15	16
增量學(xué)習(xí)方法	[42,105,106]	[42,105,106]	[42,106]	[105,106]

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