## 5 Semantic Understanding of Concept Layer
### Feature-Learning-Based Method
#### 5.3.3 The Method Based on Weak Supervised Deep Learning
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Method** & \multicolumn{2}{c|}{**Accuracy (\%)**} & \multicolumn{2}{c|}{**Other Indicators**} \\ \hline \multirow{6}{*}{Visual features-based method} & Gabor texture [161] & 76.91 & - \\ \cline{2-4}  & Color-HLS [161] & 81.19 & - \\ \cline{2-4}  & NN-STSIM [162] & 86 & - \\ \cline{2-4}  & Quaternion orientation & 85.48 \(\pm\) 1.02 & - \\ \cline{2-4}  & difference [163] & 90.6 \(\pm\) 1.4 & - \\ \cline{2-4}  & MS-CLBP [164] & 76.81 & - \\ \hline \multirow{6}{*}{Object-semantics-based method} & BoVW [161] & 77.71 & - \\ \cline{2-4}  & BoVW + SCK [161] & 77.71 & - \\ \cline{2-4}  & SPM [161] & 75.29 & - \\ \cline{2-4}  & SPCK ++ [165] & 77.38 & - \\ \cline{2-4}  & HMFF [166] & 92.38 \(\pm\) 0.62 & - \\ \cline{2-4}  & CCM-BoVW [167] & 86.64 \(\pm\) 0.81 & - \\ \cline{2-4}  & Wavelet BoVW [168] & 87.38 \(\pm\) 1.27 & - \\ \cline{2-4}  & UFL [169] & 81.67 \(\pm\) 1.23 & - \\ \cline{2-4}  & COPD [170] & 91.33 \(\pm\) 1.11 & - \\ \cline{2-4}  & FV [171] & 93.8 & - \\ \cline{2-4}  & VLAT [171] & 94.3 & - \\ \cline{2-4}  & SG-UFL [172] & 82.72 \(\pm\) 1.18 & - \\ \cline{2-4}  & PSR [173] & 89.1 & - \\ \cline{2-4}  & UFL-SC [174] & 90.26 \(\pm\) 1.51 & - \\ \cline{2-4}  & SAL-PLSA [175] & 87.62 & - \\ \cline{2-4}  & SAL-LDA [175] & 88.33 & - \\ \hline \multirow{6}{*}{Multiview DL [177]} & CaffeNet finetune [176] & 95.48 & - \\ \cline{2-4}  & GoogleNet finetune [176] & 97.1 & - \\ \cline{2-4}  & Multiview DL [177] & 93.48 \(\pm\) 0.82 & \
\begin{tabular}{c} 84.35 (Sensitivity), 91.72 \\ (Specificity) \\ \end{tabular} \\ \cline{2-4}  & GBRCN [178] & 94.53 & - \\ \cline{2-4}  & ADPM [179] & 94.86 & - \\ \cline{2-4}  & HCSAE [180] & 97.14 \(\pm\) 1.19 & - \\ \cline{2-4}  & MARTA GANs [181] & 94.86 \(\pm\) 0.80 & - \\ \cline{2-4}  & Fusion by addition [182] & 97.42 \(\pm\) 1.79 & - \\ \cline{2-4}  & salM\({}^{\prime}\)LBP-CLM [183] & 95.75 \(\pm\) 0.80 & - \\ \cline{2-4}  & TEX-Nets [184] & 97.72 & - \\ \cline{2-4}  & CCP-Net [185] & 97.52 \(\pm\) 0.97 & - \\ \cline{2-4}  & CNN (LOFs+GCFs) [186] & 99.00 \(\pm\) 0.35 & - \\ \cline{2-4}  & ARCNet-VGG16 [187] & 99.12 \(\pm\) 0.40 & - \\ \cline{2-4}  & D-CNN with VGG16 [188] & 98.93 \(\pm\) 0.10 & - \\ \cline{2-4}  & SAL-TS-Net [189] & 98.90 \(\pm\) 0.95 & - \\ \cline{2-4}  & Two-stream deep fusion [190] & 98.02 \(\pm\) 1.03 & - \\ \cline{2-4}  & PMS [191] & 98.81 &
\begin{tabular}{c} 8.32 \(\times\) 10\({}^{6}\) (Number of \\ neurons) \\ \end{tabular} \\ \cline{2-4}  & SSF-AlexNet [192] & 92.43 \(\pm\) 0.46 & - \\ \cline{2-4}  & VGG16+MSCP+MRA [193] & 98.40 \(\pm\) 0.34 & - \\ \cline{2-4}  & MCNN [194] & 96.66 \(\pm\) 0.90 & - \\ \hline Bidirectional adaptive feature fusion [195] & 95.48 & - \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of main methods in geographic scene classification. scene understanding, it is difficult to construct a large-scale, high-quality and complete HSR remote sensing image dataset for training. Firstly, from the perspective of time, a training sample can only represent the sampling of a time section. However, the interpretations of objects are dynamic in different periods. This time heterogeneity puts forward higher requirements for the quality, scale and completeness of sample annotation [196]. Secondly, from the perspective of space, due to the differences in climate and light conditions, the distribution of ground objects in different geographic scenes has natural heterogeneity [197]. This spatial heterogeneity leads to the imbalance of sample categories in the supervised learning process, whether within the training set or between the training set and the test set, which leads to \"over-fitting\" or \"under-fitting\" problems. 2. In terms of learning mechanisms, supervised learning mainly relies on semantic support provided by manual annotation as the only learning signal for model training. If human labeling is regarded as prior knowledge, the machine has been limited in knowledge in the process of labeling [198].