Incremental Deep Learning for Remote Sensing Image Interpretation
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摘要: 深度学习的发展推动了高精度遥感图像智能解译模型的涌现。然而，目前遥感智能解译模型大多基于预先定义的静态数据集独立训练，难以适应环境开放和需求动态的实际应用，严重阻碍了遥感智能解译模型的广域和长期运用。增量学习能使模型持续学习新知识，并保持对旧知识的记忆，近年来，被广泛应用于推动遥感智能解译模型演化、提升模型智能解译性能。该文面向多模态遥感数据、不同类型解译任务，全面调研了遥感图像智能解译增量学习方法，从遗忘问题解决思路、解译模型进化应用两个层面梳理了现有研究工作。在此基础上，从促进遥感图像解译模型进化研究的角度，展望和讨论了遥感领域增量学习的未来研究方向。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.
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表 1 面向遥感图像解译的增量学习方法对比与总结
方法 核心思想 优点 缺点 代表性成果 知识蒸馏 新旧模型在同一输入图像上的输出保持一致 以损失函数的形式约束模型的参数更新，简单有效且易于实施 旧模型保存，占用一定的存储空间 逐任务知识蒸馏[24] 背景建模知识蒸馏[25] 空间-通道压缩特征蒸馏[26] 结构化知识蒸馏[27,28] 历史信息引导特征蒸馏[29] 网络扩展 增加独立网络参数学习新知识 直接冻结已有网络即可有效保持旧知识 不断扩大的网络规模增加计算和存储成本 增量学习建模为提升过程[30,31] 特征编码器深层结构扩展[32] 残差模块扩展[33,34] 特征迁移模块扩展[35] 记忆回放 保留少部分旧数据，帮助模型回忆旧知识 模型基于新旧数据优化，能够较好地感知新旧知识的边界 旧数据保留增加存储成本，且易产生过拟合 代表性样本选择[36,38] 预测偏差校正[39–46] 旧数据特征生成[24,47] 
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表 2 面向可见光图像解译的类别增量学习方法总结
解译任务 研究工作 贡献 遗忘问题解决思路 知识蒸馏 网络扩展 记忆回放 场景识别 [31] 类别增量学习建模为特征提升过程，动态扩展模块化分类网络 $ \surd $ $ \surd $ $ \surd $ [38] 动态混合的样本选择策略和基于异构原型的学习框架，增加存储样本的信息量 $ \surd $ $ \times $ $ \surd $ [64] 利用可学习提示解耦场景识别的知识，避免特定知识的相互干扰缓解遗忘问题 $ \times $ $ \surd $ $ \times $ [66] 相互协作的瞬时网络和保持网络实现有效的旧知识召回和新知识积累 $ \surd $ $ \times $ $ \times $ [37] 凸包构造算法选取接近类边界的样本 $ \times $ $ \times $ $ \surd $ [53] 根据新旧类别的相似性设计类别学习顺序，提高新模型的新类别学习效率 $ \surd $ $ \times $ $ \surd $ [47] 利用VAE生成多样的旧类别特征，避免过拟合和存储成本高的问题 $ \times $ $ \times $ $ \surd $ [40] 平衡的旧数据集微调新模型的预测头，缓解新旧类别不平衡导致的预测偏差 $ \surd $ $ \times $ $ \surd $ [63] 扩展预测头学习新类别，并依据图像特征与任务原型的相似性选择预测头 $ \surd $ $ \surd $ $ \times $ 目标检测 [45] 基于熵的蓄水池抽样策略和样本抽样加权缓解回放不平衡导致的预测偏差 $ \times $ $ \times $ $ \surd $ [49] 在区域候选网络和预测头添加分支并迁移知识，实现新类别学习和旧知识保留 $ \surd $ $ \surd $ $ \times $ 地物分类 [28] 跨图像特征相关性蒸馏损失增强模型的新类别学习能力 $ \surd $ $ \times $ $ \times $ [24] 像素级旧特征生成，应对遗忘问题；逐任务知识蒸馏避免新类别向旧类别压缩 $ \surd $ $ \times $ $ \surd $ [26] 空间-通道维度的特征压缩并迁移，降低特征空间知识蒸馏的计算成本；信息熵量化旧模型预测的准确性，并仅使用高置信度像素预测维持旧知识 $ \surd $ $ \times $ $ \times $ [29] 历史信息引导模型关注前景(旧类别)区域的知识迁移；高置信度的旧模型预测与真实标签相结合，为新模型提供完整的类别监督信息 $ \surd $ $ \times $ $ \times $ [69] 多样蒸馏损失促使模型关注易被遗忘的小目标和目标边缘 $ \surd $ $ \times $ $ \times $ [67] 依据类别实例数量计算每张图像的重要性，确保存储图像的类别均衡 $ \surd $ $ \times $ $ \surd $
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表 3 面向可见光遥感图像解译的类别增量学习常用数据集
解译任务 数据集 图像数量 类别数量 类别增量学习方法 场景识别 NWPU-RESISC45[77] 31500 45 [37,53,64] FGSCR-42[78] 9320 42 [38] PatternNet[79] 30400 38 [53] RSICB-256[80] 28000 35 [31,66] Optimal-31[81] 1860 31 [47,63] AID[75] 10000 30 [31,64,66] CLRS[74] 15000 25 [40] UC-Merced[70] 2100 21 [31,47,63,64,66] SIRI-WHU[82] 2400 12 [37] 目标检测 DIOR[71] 23463 20 [45,49] DOTA[83] 2806 15 [45,49] NWPU VHR-10[84] 800 10 [45] 地物分类 iSAID[76] 2806 15 [24,28,29] GCSS[85] 948 8 [29] Deepglobe[86] 1146 7 [24,26] Potsdam[72]/Vaihingen[73] 38/33 6 [24,26,28,67,69] Luxcarta[67] - 5 [67,68]
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表 4 面向可见光图像解译的类别增量学习方法性能对比
解译任务 研究工作 评价指标 数据集 增量训练次数 得分(%) 发布时间 场景识别 [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 [66] mACC RSICB-256 9 82.63 TGRS’2022 AID 6 88.93 UC-Merced 3 89.52 [37] ACC NWPU-RESISC45 7 93.47 TGRS’2022 SIRI-WHU 7 98.13 [53] mACC NWPU-RESISC45 9 49.42 JSTARS’2021 PatternNet 6 62.31 [47] ACC Optimal-31 10 86.80 GRSL’2021 UC-Merced 7 94.20 [40] ACC CLRS 4 32.30 CIOP’2021 [63] ACC Optimal-31 10 71.00 GRSL’2020 UC-Merced 7 89.00 目标检测 [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 DOTA 2 65.20 地物分类 [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 GCSS 5 62.53 [69] mIoU Potsdam 2 75.92 TGRS’2021 Vaihingen 3 73.96 [67] F1 Luxcarta 3 68.09 JSTARS’2019 Potsdam 3 84.25 Vaihingen 3 87.44
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表 5 面向可见光图像解译的域增量学习方法总结
解译任务 研究工作 贡献 遗忘问题解决思路 知识蒸馏 网络扩展 记忆回放 场景识别 [87] 双网络知识协同学习策略增强场景识别模型的新知识学习和旧知识巩固能力 $ \surd $ $ \times $ $ \times $ 目标检测 [88] 为特征空间、输出空间的知识蒸馏添加可学习权重，解决预测偏差问题 $ \surd $ $ \times $ $ \surd $ 地物分类 [30] 域增量学习建模为提升过程，并利用自适应学习率确定每个网络的重要性 $ \times $ $ \surd $ $ \times $ [50] 扩展整个网络，新网络的学习目标是弥补已有模型在新数据上的性能不足 $ \times $ $ \surd $ $ \times $ 变化检测 [34] 输出空间和多层次特征空间的知识蒸馏保留旧知识；扩展域残差单位和解码器，学习新知识 $ \surd $ $ \surd $ $ \times $
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表 6 面向可见光图像解译的域增量学习方法性能对比
解译任务 研究工作 评价指标 数据集 增量训练次数 得分(%) 发布时间 场景识别 [87] ACC NWPU-RESISC45[77] 5 80.53 计算机应用’2023 AID[75] 5 77.40 目标检测 [88] mAP@0.5 FASDD_CD[89]$ \to $FASDD_RS[89] 2 49.47 JAG’2023 FASDD_RS[89]$ \to $FLAME[90] 2 51.53 地物分类 [30] OA DREAM-B$ ‡ $[30]$ \to $xBD[91]$ \to $Haiti-xBD[30] 3 97.94
(仅新域)ISPRS’2023 [50] IoU DREAM-B[50] 4 63.72 Remote Sens.’2020 变化检测 [34] $ {\Delta }_{\mathrm{I}\mathrm{o}\mathrm{U}} $ SYSU-CD[92]$ \to $CDD[93]$ \to $PRCV[34] 3 8.22 TGRS’2024 $ \to $：指示模型增量学习顺序
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表 8 面向可见光图像解译的任务增量学习性能对比
解译任务 研究工作 评价指标 数据集 增量训练次数 得分(%) 发布时间 场景识别 [35] mACC AID[75] 10 86.74 TGRS’2022 BigEarthNet[94] 5 95.89 EuroSAT[95] 2 94.85 EuroSAT[95]$ \to $BigEarthNet[94]$ \to $RS-C11[96]$ \to $
RSSCN7[97]$ \to $AID[75]$ \to $SIRI-WHU[82]$ \to $SAT-4[98]7 79.86 地物分类 [33] $ {\Delta }_{\mathrm{m}\mathrm{I}\mathrm{o}\mathrm{U}} $ GID[99]$ \to $BDCI2020[100]$ \to $Deepglobe[86]$ \to $
LoveDA-Urban[101]$ \to $LoveDA-Rural[101]5 -5.46 Remote Sens.’2023 [27] mIoU Deepglobe[86]$ \to $Potsdam[72]$ \to $GCSS[85] 3 66.27 TGRS’2021 Vaihingen[73]$ \to $Potsdam[72] 2 79.72 $ \to $：指示模型增量学习顺序
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表 9 面向合成孔径雷达图像目标识别的类别增量学习方法总结
研究工作 贡献 遗忘问题解决思路 知识蒸馏 网络扩展 记忆回放 [102] 基于广义最大覆盖的样本选择，降低计算成本 $ \times $ $ \times $ $ \surd $ [56] 基于局部分布统计信息和全局分布密度信息选择代表性样本；评估测试样本的预测可靠性，并由此管理增量数据 $ \times $ $ \times $ $ \surd $ [32] 特征编码器深层结构扩展结合记忆回放、知识蒸馏，应对遗忘问题 $ \surd $ $ \surd $ $ \surd $ [44] 训练样本抽样加权和记忆增强的权重对齐，解决新旧类别不平衡导致的预测偏差 $ \surd $ $ \times $ $ \surd $ [54] Openmax算法帮助模型识别未知类别，此后利用记忆回放赋予模型持续学习未知类别的能力 $ \times $ $ \times $ $ \surd $ [39] 可分离学习策略缓解新旧类别不平衡导致的预测偏差 $ \surd $ $ \times $ $ \surd $ [41] 样本抽样加权，构建类别均衡的训练批次，校正预测偏差 $ \surd $ $ \times $ $ \surd $ [43] 类别分离损失解决新旧类别相似产生的混淆问题；偏差校正层应对新旧类别不平衡现象 $ \surd $ $ \times $ $ \surd $ [46] 类别的有效样本数加权交叉熵损失，解决新旧类别不平衡导致的预测偏差 $ \times $ $ \times $ $ \surd $ [36] 基于局部几何和统计信息的类边界样本选择，并利用SMOTE方法重采样，丰富旧类别样本 $ \times $ $ \times $ $ \surd $ [55] 基于局部几何和统计信息的类边界样本选择 $ \times $ $ \times $ $ \surd $
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表 10 面向合成孔径雷达图像目标识别的类别增量学习方法性能对比
研究工作 网络架构 评价指标 数据集 每类存储量 增量训练次数 得分(%) 发布时间 [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$ + $OpenSARShip 200(11个类别) 12 93.87 GRSL’2023 [54] CNN OA MSTAR – 3 92.70 RadarConf’2023 [39] DCFM-CNN ACC MSTAR 30 7 91.76 TGRS’2022 OpenSARShip 30 3 – [41] ResNet-18 ACC MSTAR$ + $OpenSARShip 200(12个类别) 12 93.87 GRSL’2023 [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个类别) 8 86.50 TGRS’2019 $ + $：组合不同数据集模拟增量学习阶段
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表 12 面向高光谱图像分类的增量学习方法性能对比
增量学习类型 研究工作 评价指标 数据集 增量训练次数 得分(%) 发布时间 类别增量学习 [42] ACC PaviaU 3 85.76 Remote Sens.’2022 Salinas 5 93.31 Houston 4 86.54 [108] ACC PaviaU 2 73.06 TCybernatics’2020 Indian Pines – – Salinas – – 任务增量学习 [109] mACC PaviaU$ \to $Indian Pines
$ \to $Salinas$ \to $Houston4 72.84 TGRS’2022 $ \to $：指示模型增量学习顺序
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