光学学报, 2019, 39 (8): 0828002, 网络出版: 2019-08-07 复制并引用该论文  被引通知

图 & 表

图 1. 本文方法的整体框架图

Fig. 1. Whole framework of proposed method

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图 2. 本文方法运算流程图

Fig. 2. Operational flow chart of proposed method

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图 3. UCM数据集若干类的样本。(a)农田;(b)飞机;(c)棒球场;(d)密集住宅;(e)高速公路;(f)海港;(g)储罐;(h)网球场;(i)立交桥;(j)高尔夫球场

Fig. 3. Images of several classes from UCM dataset. (a) Agricultural; (b) airplane; (c) baseball diamond; (d) dense residential; (e) freeway; (f) harbor; (g) storage tanks; (h) tennis court; (i) overpass; (j) golf course

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图 4. AID数据集若干类的样本。(a)机场;(b)贫瘠地;(c)海滩;(d)桥梁; (e)商业区;(f)运动场;(g)池塘;(h)火车站;(i)体育场;(j)立交桥

Fig. 4. Images of several classes from AID dataset. (a) Airport; (b) bare land; (c) beach; (d) bridge; (e) commercial; (f) playground; (g) pond; (h) railway station; (i) stadium; (j) viaduct

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图 5. RSSCN7数据集类的样本。(a)草地;(b)河湖;(c)工厂;(d)场地;(e)森林;(f)居民区;(g)停车场

Fig. 5. Images of several classes from RSSCN7 dataset. (a) Grass; (b) river laker; (c) industrial; (d) field; (e) forest; (f) residential; (g) parking

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图 6. 不同模型词向量融合的结构对齐效果

Fig. 6. Structure alignment performance of word vector fusion with different models

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图 7. 不同语料词向量融合的结构对齐效果图

Fig. 7. Structure alignment performance of word vector fusion with different corpora

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图 8. UCM和AID数据集上本文方法在不同α值上的OA值。(a) UCM上不同训练模型词向量融合;(b) UCM上不同训练语料词向量融合;(c) AID上不同训练模型词向量融合;(d) AID上不同训练语料词向量融合

Fig. 8. OA values of proposed method for different α on UCM and AID datasets. (a) Fusion of word vectors from different training models on UCM dataset; (b) fusion of word vectors from different training corpora on UCM dataset; (c) fusion of word vectors from different training models on AID dataset; (d) fusion of word vectors from different training corpora on AID dataset

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图 9. 不同训练模型词向量和不同训练语料词向量的各unseen类融合效果。(a) UCM上不同训练模型词向量融合;(b) UCM上不同训练语料词向量融合;(c) AID上不同训练模型词向量融合;(d) AID上不同训练语料词向量融合

Fig. 9. Fusion performance of different training models and different training corpora on unseen classes. (a) Fusion of word vectors from different training models on UCM dataset; (b) fusion of word vectors from different training corpora on UCM dataset; (c) fusion of word vectors from different training models on AID dataset; (d) fusion of word vectors from different training corpora on AID dataset

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图 10. 本文方法及对比方法的各unseen类S1+S2+S3词向量融合效果。 (a) UCM数据集; (b) AID数据集

Fig. 10. Fusion performance of S1+S2+S3 word vectors on unseen classes by proposed method and relative methods. (a) UCM dataset; (b) AID dataset

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图 11. 测试遥感图像I的场景ZSC效果图

Fig. 11. Scene ZSC results of test remote-sensing image I

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图 12. 测试遥感图像II的场景ZSC效果图

Fig. 12. Scene ZSC results of test remote-sensing image II

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表 1不同训练模型词向量和不同训练语料词向量融合前后的OA

Table1. OA values of different training models and different training corpora before and after fusion of word vectors%

Dataset Fusion of word vectors from different models Fusion of word vectors from different corpora gl wv gl􀱇wv Wiki Crawl Wiki􀱇Crawl UCM 51.39 47.65 61.23 51.39 42.86/44.61 59.77 AID 57.92 61.53 69.47 57.92 56.16/58.29 68.49

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表 2本文方法及对比方法OA值

Table2. OA values of proposed method and relative methods%

Method UCM AID S1 S2 S3 S1+S2 S2+S3 S1+S2+S3 S1 S2 S3 S1+S2 S2+S3 S1+S2+S3 LatEm[4] 18.80 20.40 19.80 33.00 23.00 20.80 15.90 22.65 23.81 18.71 28.17 21.62 RKT[5] 40.00 39.80 44.60 40.20 43.60 43.60 48.92 48.03 48.15 48.92 50.13 53.25 DMaP[9] 38.20 39.60 41.60 40.80 42.00 40.20 39.24 43.44 38.54 46.67 45.22 44.97 BiDiLEL[8] 28.51 33.48 39.20 40.40 40.00 41.00 32.91 42.55 32.40 47.85 50.44 49.63 JLSE[6] 37.25 34.21 45.68 37.66 34.88 38.03 36.11 34.97 42.30 35.99 43.50 45.54 SSE[7] 38.36 39.48 37.91 38.72 39.19 38.23 38.24 37.16 39.56 34.53 40.92 43.56 Proposed 44.61 47.65 51.39 61.16 61.23 68.56 58.29 61.53 57.92 70.44 69.47 76.85

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表 3各ZSC算法在AID数据集上对S1词向量上的运算耗时

Table3. Computing time of different ZSC algorithms on AID dataset with S1 word vector

Method Time/s LatEm[4] 21.66 RKT[5] 24.24 DMaP[9] 409.26 BiDiLEL[8] 28.81 JLSE[6] 70.20 SSE[7] 19.74 Proposed 17.90

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吴晨, 于光, 张凤晶, 刘宇, 袁昱纬, 全吉成. 基于词向量一致性融合的遥感场景零样本分类方法[J]. 光学学报, 2019, 39(8): 0828002. Chen Wu, Guang Yu, Fengjing Zhang, Yu Liu, Yuwei Yuan, Jicheng Quan. Zero-Shot Classification Method for Remote-Sensing Scenes Based on Word Vector Consistent Fusion[J]. Acta Optica Sinica, 2019, 39(8): 0828002.