### 6.7.4. RNN model with permuted input for MSTS images based crop classification

Permuted RNN model is similar to the conventional RNN model (RNN-v1) discussed in section 6.5.4 apart from the addition of a permutation layer. The permutation layer prepares an ensemble of permuted spectral bands similar to as in Perm-1D-CNN-v1 or Perm-3D-CNN-v1. Therefore, the input to this RNN model namely Perm-RNN-v1 is a 7×120 matrix where 7 indicates the length of time dimension (time stamps) and 120 indicates to the permuted and ensembled spectral depth in data in each time stamp. Since, RNNs do not accommodate spatial contextual information, per-pixel approach is considered here. A simple LSTM unit is selected as recurrent cell. Similar to RNN-v1, seven LSTM units constitute the R1 recurrent layer and the architecture is used in a many-to-many fashion. It means the input to the recurrent layer is more than one dimensional (in this case, 7) and output from the recurrent layer is more than one dimensional (in this case, 7). Also, the flow of activation from each recurrent cell in the recurrent layer is considered in a bidirectional manner. It means that the activation from particular recurrent cell can move to next recurrent cell (next time stamp, usual) and previous recurrent cell (previous time stamp). This helps in the crop classification and the significance is discussed in the results and discussion section later in this chapter. Further a FC layer with 20 nodes and an output layer with 6 outputs is set. The Perm-RNN-v1 architecture are listed in Table 6.9. The hyper-parameter settings are similar to RNN-v1 are also listed in Table 6.9.

<table><caption>Table 6.9 Architecture and hyperparameter settings of RNN model with permuted input for MSTS images based crop classification.</caption><thead><tr><th rowspan="2">Model</th><th colspan="5">Layer</th></tr><tr><th>Input</th><th>Permutation layer</th><th>Recurrent layer (R1)</th><th>Fully connected layer (FC)</th><th>Output layer</th></tr></thead><tbody><tr><td>Perm-RNN-v1</td><td>Input data size = 7×12 (time, spectral)<br>Normalization = Batch Normalization</td><td>Permutated sets = 10<br>Kernel size = 12 (spectral)<br>Activation = Linear Time-distributed: Yes</td><td>Filters = 10<br>Kernel size = 7 (time)<br>Activation = Tanh<br>Dropout fraction = 0.1<br>Pooling size = 0<br>Bidirectional: Yes<br>Input-output format: Many-to-many</td><td>Nodes= 20<br>Activation = ReLU<br>Dropout fraction = 0.1<br>Time-distributed: Yes</td><td>Labels = 6<br>Activation = Softmax<br>Time-distributed: Yes</td></tr><tr><td>Training parameters</td><td colspan="5">Loss function = Categorical cross entropy<br>Learning rate = 0.01<br>Number of epochs = 200<br>Optimizer = Adam [211]<br>10 fold cross validation strategy is employed during training</td></tr></tbody></table>
### 6.7.5. CNN-RNN models with permuted local spectral convolution for MSTS images based crop classification

In this section, CNN and RNN architectures are used synergistically to form a novel CNN-RNN model for crop classification with MSTS satellite images. These CNN-RNN models are similar to CNN-RNN models discussed in section 6.5.5 with the difference of addition of a permuted layer. The ensemble of permuted sets of spectral bands help emphasize the spectral characteristic during MSTS image based classification as discussed in section 6.6.

#### 6.7.5.1. 1D-CNN-RNN model with permuted local spectral convolution for MSTS images based crop classification

In this 1D-CNN-RNN model namely Perm-1D-CRNN-v1, first, a permutation layer which generates 10 permuted versions of spectral bands. Then, the 10 permuted versions are stacked together to form an ensembled spectral band stack. This stack of bands (120, 10 permutations with 12 bands in each permutation set) is used as input.

After the ensembled input is generated, one dimensional convolution layers are used. Since, the temporal depth is unaltered, one dimensional convolution in the temporal dimension will yield similar results as 1D-CRNN-v1, it is insignificant to replicate the model. However, the spectral depth has now increased from 12 to 120, local spectral convolutions should yield more and may be different spectral features. Therefore, a series of spectral convolutions are performed over the spectral dimension with the help of one dimensional convolutional filters.

Three convolutional layers in time-distributed fashion are employed. The kernel size of the filters in the three convolutional layers i.e. C1, C2, and C3 layers are set to 7, 21, and 63 respectively. The kernel sizes are set based on experiments.

Then, a conventional RNN model is considered. Since, 1D-CNNs or RNNs cannot accommodate spatial contextual information, per-pixel approach is considered here. A simple LSTM unit is selected as recurrent cell. Since the temporal depth is 7, seven LSTM units constitute the R1 recurrent layer. Here again, the RNN architecture is used in a many-to-many fashion. Also, the flow of activation from each recurrent cell in the recurrent layer is considered in a bidirectional manner. Then, a FC layer with 20 nodes is used. Finally, an output layer with 6 classes is used. The FC and the output layer are also employed in time-distributed fashion.

The architecture and hyper-parameter settings are listed in Table 6.10. ReLU, hyperbolic tangent (Tanh) and softmax activation functions are used in the convolutional & FC, LSTM recurrent cells, and output layer respectively. A dropout strategy is considered for regularization and dropout fraction values of 0.2, 0.1 and 0.1 for (C1, C2, C3), R1 and FC are set respectively.
The loss function used is the categorical cross entropy function. It optimizes the classification accuracy based on probabilities of each class during classification. The learning rate and the number of epochs are set to 0.01 and 200 respectively.

### 6.7.5.2. 2D-CNN-RNN model with permuted input for MSTS images based crop classification

In this 2D-CNN-RNN model namely Perm-2D-CRNN-v1, first, a permutation layer which generates 10 permuted versions of spectral bands. Then, the 10 permuted versions are stacked together to form an ensembled spectral band stack. This stack of bands (120, 10 permutations with 12 bands in each permutation set) is used as input.

After the ensembled input is generated, a two dimensional convolution layer is used. Since, two dimensional convolutions can accommodate spatial contextual information, patch based convolution approach is considered here. The kernel size of the filter in the convolutional layer i.e. C1 layer is a 3×3 window. The convolutional layer is employed in time-distributed fashion.

Then, a conventional RNN model is considered identical to the RNN model used in Perm-1D-CRNN-v1. The architecture of the Perm-2D-CRNN-v1 is listed in Table 6.10. The hyper-parameter settings are similar to the Perm-1D-CRNN-v1 model and are listed in Table 6.10.

### 6.7.5.3. 3D-CNN-RNN model with permuted local spectral convolution for MSTS images based crop classification

In this 3D-CNN-RNN model namely Perm-3D-CRNN-v1, first, a permutation layer which generates 10 permuted versions of spectral bands. Then, the 10 permuted versions are stacked together to form an ensembled spectral band stack. This stack of bands (120, 10 permutations with 12 bands in each permutation set) is used as input.

After the ensembled input is generated, a series of three dimensional convolution layers are used. The three dimensional convolution layers are used here to extract spectral-spatial features as the temporal features are captured by the RNN architecture. Since, three dimensional convolutions can also accommodate spatial contextual information, patch based convolution approach is considered here. The kernel size of the filters in the three convolutional layers i.e. C1, C2, and C3 layers are set to 3×3×7, 1×1×21, and 1×1×63 respectively. The kernel sizes are set based on experiments.

Then, a conventional RNN model is considered identical to the RNN model used in Perm-1D-CRNN-v1 and Perm-2D-CRNN-v1. The architecture of the Perm-3D-CRNN-v1 is listed in Table 6.10. The hyper-parameter settings are similar to the Perm-3D-CRNN-v1 model and are listed in Table 6.10.
<table><caption>Table 6.10 Architecture and hyperparameter settings of CNN-RNN with permuted spectral convolution based models for MSTS images based crop classification.</caption><thead><tr><th rowspan="2">Model</th><th colspan="6">Layer</th></tr><tr><th>Input</th><th>Permutation layer</th><th>Convolutional layer 1 (C1, C2, C3)</th><th>Recurrent layer (R1)</th><th>Fully connected layer (FC)</th><th>Output layer</th></tr></thead><tbody><tr><td>Perm-1D-CRNN-v1</td><td>Input data size = 7×12 (time, spectral)<br>Normalization: Batch<br>Normalization</td><td>Permutated sets = 10<br>Kernel size = 12 (spectral)<br>Activation = Linear Time-distributed: Yes</td><td>Filters = 20, 40, 60<br>Kernel size = 7, 21, 63 (spectral)<br>Activation = ReLU<br>Dropout fraction = 0.2 Time-distributed: Yes</td><td>Filters = 10<br>Kernel size = 7 (time)<br>Activation = Tanh<br>Dropout fraction = 0.2<br>Pooling size = 0<br>Bidirectional: Yes Input-output format: Many-to-many</td><td>Nodes= 20<br>Activation = ReLU<br>Dropout fraction = 0.1<br>Time-distributed: Yes</td><td>Labels = 6<br>Activation = Softmax Time-distributed: Yes</td></tr><tr><td>Perm-2D-CRNN-v1</td><td>Input data size = 7×3×3×12 (time, spatial, spectral)<br>Normalization: Batch<br>Normalization</td><td>Permutated sets = 10<br>Kernel size = 12 (spectral)<br>Activation = Linear Time-distributed: Yes</td><td>Filters = 32<br>Kernel size = 3×3 (spatial, spatial)<br>Activation = ReLU<br>Dropout fraction = 0.2 Time-distributed: Yes</td><td>Filters = 10<br>Kernel size = 7 (time)<br>Activation = Tanh<br>Dropout fraction = 0.2<br>Pooling size = 0<br>Bidirectional: Yes Input-output format: Many-to-many</td><td>Nodes= 20<br>Activation = ReLU<br>Dropout fraction = 0.1<br>Time-distributed: Yes</td><td>Labels = 6<br>Activation = Softmax Time-distributed: Yes</td></tr><tr><td>Perm-3D-CRNN-v1</td><td>Input data size = 7×3×3×12 (time, spatial, spectral)<br>Normalization: Batch<br>Normalization</td><td>Permutated sets = 10<br>Kernel size = 12 (spectral)<br>Activation = Linear Time-distributed: Yes</td><td>Filters = 20, 30, 40<br>Kernel size = 3×3×7, 1×1×21, 1×1×63 (spatial, spatial, spectral)<br>Activation = ReLU<br>Dropout fraction = 0.2 Time-distributed: Yes</td><td>Filters = 10<br>Kernel size = 7 (time)<br>Activation = Tanh<br>Dropout fraction = 0.2<br>Pooling size = 0<br>Bidirectional: Yes Input-output format: Many-to-many</td><td>Nodes= 20<br>Activation = ReLU<br>Dropout fraction = 0.1<br>Time-distributed: Yes</td><td>Labels = 6<br>Activation = Softmax Time-distributed: Yes</td></tr><tr><td>Training parameters</td><td colspan="6">Loss function = Categorical cross entropy<br>Learning rate = 0.01<br>Number of epochs = 200<br>Optimizer = Adam [211]<br>10 fold cross validation strategy is employed during training</td></tr></tbody></table>
# 6.8. Results and Discussion

## A. Quantitative analysis

The results from the models presented in section 6.5, and 6.7 are discussed in this section. Both, qualitative and quantitative assessment are carried out. Overall, results from 12 different models are compared and discussed here. These are 1D-CNN-v1, 3D-CNN-v1, RNN-v1, 1D-CRNN-v1, 2D-CRNN-v1, 3D-CRNN-v1, Perm-1D-CNN-v1, Perm-3D-CNN-v1, Perm-RNN-v1, Perm-1D-CRNN-v1, Perm-2D-CRNN-v1, and Perm-3D-CRNN-v1. Figure 6.10 show classification results obtained using these 12 models. Also, Table 6.11 shows quantitative assessment of the classification performance of these 12 models. It is evident from Table 6.11 that Perm-1D-CNN-v1 shows the best classification accuracy of 99.70%. However, most of the other models also show classification accuracies of the same order i.e. above 90%. Therefore, it is difficult to recognize the best model with only quantitative assessment. A detailed qualitative assessment is carried out with the help of ground truth information.

<table><caption>Table 6.11 Summary of the classification performance of the compared classifiers.</caption><thead><tr><td>Method</td><td>Overall accuracy (%)</td></tr></thead><tbody><tr><td>1D-CNN-v1</td><td>95.66</td></tr><tr><td>3D-CNN-v1</td><td>97.45</td></tr><tr><td>RNN-v1</td><td>87.29</td></tr><tr><td>1D-CRNN-v1</td><td>89.70</td></tr><tr><td>2D-CRNN-v1</td><td>94.20</td></tr><tr><td>3D-CRNN-v1</td><td>96.00</td></tr><tr><td>Perm-1D-CNN-v1</td><td>99.70</td></tr><tr><td>Perm-3D-CNN-v1</td><td>99.15</td></tr><tr><td>Perm-RNN-v1</td><td>94.73</td></tr><tr><td>Perm-1D-CRNN-v1</td><td>96.85</td></tr><tr><td>Perm-2D-CRNN-v1</td><td>95.98</td></tr><tr><td>Perm-3D-CRNN-v1</td><td>96.00</td></tr></tbody></table>
<figure><img src="image_65.png" /><figcaption>Figure 6.10 Classified image; (a) 1D-CNN-v1. Markers; 1: GTI-1, 2: GTI-2, 3: GTI_3, and 4: GTI-4. (b) 3D-CNN-v1, (c) RNN-v1, (d) 1D-CRNN-v1, (e) 2D-CRNN-v1, (f) 3D-CRNN-v1, (g) Perm-1D-CNN-v1, (h) Perm-3D-CNN-v1, (i) Perm-RNN-v1, (j) Perm-1D-CRNN-v1, (k) Perm-2D-CRNN-v1, and (l) Perm-3D-CRNN-v1.</figcaption></figure>

## B. Qualitative assessment

A set of images collected during the crop growing season are used as ground truth to visually evaluate the performance of these models. The visual interpretation is an evaluation of the generalization performance of the models. Few of these ground truth images are discussed here. The subset areas selected for visual inspection are marked in Figure 6.6, Figure 6.7, and Figure 6.10(a). These subset areas are termed as ground truth images (GTI). Figure 6.11 shows a ground truth image (GTI-1) which is patch from the original study area (marked as 1 in Figure 6.6). The GTI-1 has two components; the first component is a GTI from November, 2017 and
the second component is GTI from February, 2018. It can be observed from GTI of November, 2017 that no major crop is growing in the area and the green patches and tree lines are popular plantations (highlighted in yellow box). Whereas, in the GTI of February, 2018, wheat crop is grown in the area (sample highlighted in green box). Visual interpretation of the corresponding patches from the classified images of the compared models indicate that all the models have correctly classified the crop as wheat. However, it is critical to observe that though Perm-1D-CNN-v1 or Perm-3D-CNN-v1 does not perform temporal convolution, they able to correctly classify the image patch. This indicates to the ability of CNNs to focus on any particular time stamp based on training labels and provide classification. Even though, Perm-1D-CNN-v1 show highest classification accuracy, it inability to not consider temporal pattern makes it less generalized model in MSTS image based crop classification.

Figure 6.12 shows a ground truth image (GTI-2) which is patch 2 from the original study area. The GTI-2 also has two components; the first component is a GTI from November 2017 and the second component is GTI from February, 2018. It can be observed from GTI of November, 2017 that major crop growing in the area is sugarcane (highlighted in yellow box) and some fallow lands are present. Whereas, in the GTI of February, 2018 that wheat crop is grown in these fallow lands (highlighted in green box) and most of the sugarcane is harvested which in turn leaves new fallow lands. Visual interpretation of the corresponding patches from the classified images of the compared models reveal peculiar information.

<figure><img src="image_66.png" /><figcaption>Figure 6.11 Visual interpretation and evaluation of classification performance of the compared classifiers for GTI-1.</figcaption></figure>
Models with only convolutional layers and no recurrent layers such as 1D-CNN-v1 or 3D-CNN-v1 focus on a particular time stamp and does not exploit the temporal pattern present in the MSTS data. For example, in Figure 6.12, 3D-CNN-v1 (in both, standard and permuted input case) classifies the temporary fallow lands (unused land in between two crop cycles) as grasslands (shown in yellow). On the contrary, models with recurrent layers such as RNN-v1 or 3D-CRNN-v1 are able to ignore the temporary fallow lands and classified the areas as either sugarcane or wheat. It is important to emphasize here that even though the Perm-1D-CNN-v1 provides highest classification accuracy, it is unable to capture the temporal pattern in the MSTS data. Therefore, it is obvious to infer that standalone convolutional models are overfitting.

It can also be observed from Figure 6.12 that all the standard input based models have misclassified few sugarcane fields (highlighted in red box) as built-up due to dominance of HH backscatter response in the spectral dimension. Whereas, the Perm-RNN-v1 and Perm-3D-CRNN-v1 models have correctly classified the same sugarcane fields. This indicates that models such as Perm-RNN-v1 or Perm-3D-CRNN-v1 are able to capture the spectral attributes more efficiently because of the training of these models with ensemble of permuted spectral bands (averaging out the dominance of any particular spectral band). This novel approach of permuted spectral stacking improved the generalization ability of these models and complemented the temporal attribute of the crops.

<figure><img src="image_67.png" /><figcaption>Figure 6.12 Visual interpretation and evaluation of classification performance of the compared classifiers for GTI-2.</figcaption></figure>
<figure><img src="image_68.png" /><figcaption>Figure 6.13 Visual interpretation and evaluation of classification performance of the compared classifiers for GTI-3.</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>Figure 6.14 Visual interpretation and evaluation of classification performance of the compared classifiers for GTI-4.</figcaption></figure>
Similarly, in Figure 6.13, the cropland highlighted in yellow box in the ground truth image (GTI-3) is a sugarcane field and is classified as sugarcane by the Perm-3D-CRNN-v1. In contrast, the Perm-1D-CNN-v1 model focused more on the spectral information due to the permuted spectral band stacking than on the temporal information and hence, classified the sugarcane field on the basis of a particular time stamp and not on the basis of the entire temporal profile. This analysis also indicates to the overfitting nature of convolutional only models in case of MSTS images based crop classification. The efficient exploitation of the spectral, spatial, and temporal information present in the MSTS images by the Perm-3D-CRNN-v1 is also evidenced from the correct classification of popular plantations in the subset image (highlighted in red box). On the other hand, Perm-1D-CNN-v1 misclassifies it as built-up.

The GTI in Figure 6.14 (GTI-4) shows crop fields with lots of tree lines in between (highlighted in red lines) and forests. Once again, an efficient exploitation of the spectral, spatial, and temporal information in the MSTS images is done by the Perm-3D-CRNN-v1 model as it retains the spectral and structural identities of tree line present in between crop field while suppressing the temporary fallow lands spectral attributes at the same time.

In summary, the visual interpretations highlight the overfitting nature of the otherwise quantitatively the best classification model i.e. Perm-1D-CNN-v1. Also, these interpretations highlight the good generalization ability of the proposed Perm-3D-CRNN-v1 model which is an important attribute of a classifier. It is important here to mention that the overall classification accuracy of the Perm-3D-CRNN-v1 model (96.00%) is only 3.7% less than the Perm-1D-CNN-v1 (99.70%). Hence, it is not obnoxious to assume that the Perm-3D-CRNN-v1 model is the best classifier among all the developed and compared classifiers.

## 6.9. Conclusion

This chapter presents a comprehensive study and analysis of the performance of conventional CNN and RNN models in MSTS images based crop classification. The analysis indicated to satisfactory performance of conventional CNN and RNN models in MSTS based crop classification. Also, preferences given to temporal and spatial characteristics of MSTS images by the conventional DNN models is observed. This led to underutilization of spectral characteristics of MSTS images and therefore resulted in less generalized crop classification models. To address this issue, novel CNN and RNN models are proposed and developed on the concept of local spectral convolutions on ensemble stack of permuted spectral bands of MSTS images. The local spectral convolutions in an ensembled spectral dimension improves the generalization ability of DNN models and also complements the spatial and temporal features. Overall, 12 CNN and RNN models are developed during the study namely 1D-CNN-v1, 3D-
CNN-v1, RNN-v1, 1D-CRNN-v1, 2D-CRNN-v1, 3D-CRNN-v1, Perm-1D-CNN-v1, Perm-3D-CNN-v1, Perm-RNN-v1, Perm-1D-CRNN-v1, Perm-2D-CRNN-v1, and Perm-3D-CRNN-v1.

These models are trained on MSTS satellite images of areas having crop fields in challenging landscape. Wheat and sugarcane are the major crops practices in the area considered here for study. The area, apart from croplands, also contain, forests, built-up, grasslands, and deciduous plantations. The study area is a good representation of real world crop classification scenarios particularly in India. The performance of these models in this study area presents be good indicator of their generalization ability.

Quantitative analysis of the classification results from these models indicate to superior performance of the Perm-1D-CNN-v1 model with classification accuracy of 99.7%. However, the Perm-3D-CRNN-v1, which provides 96.00% classification accuracy, indicate to have higher generalization ability during qualitative and visual assessments. The spectral and spatial attributes of land covers (e.g. built-up or popular plantations) is well captured by the Perm-3D-CRNN-v1 whereas the Perm-1D-CNN-v1 appears to be overfitting. Therefore, the Perm-3D-CRNN-v1 is the best model among the classifiers considered.
CONCLUSIONS AND FUTURE SCOPE

The main objective of this thesis is the study and critical analysis of the performance of conventional CNN and RNN models and further development of the novel CNN and RNN models to exploit the spectral and/or spatial and/or temporal information present in unimodal/single senor or multimodal/multisensor satellite images. Special focus during the development of novel DNN models is given to develop models showing better generalization performance in mixed class scenarios.

***Contributions of the thesis***

In the attempt, four tasks are set to forward the stated objective. The tasks are based on; type(s) of satellite image(s) used, application for which the task is realized, multi-modality images involvement, and presence of time series information. The tasks are satisfactorily completed and are presented in four chapters i.e. chapter 3, 4, 5, and 6.

In *Chapter 3*, a study where an attempt is made to extract information from one of the most popular satellite image modality i.e. PolSAR images is presented. The study is composed of three sub-tasks.

Under the first sub task i.e. *sub task 1*, the investigation of polarization signatures (PSs) as potential PolSAR features for land cover (LC) classification in mixed class scenarios is done. Novel PSs based features i.e. PSCFs are computed based on the degree of similarity between observed and standard PSs. Further, PSCFs are analysed, and are compared with decomposition based features for the purpose of LC classification. Comparisons indicate potential of PSCFs as significant features for LC classification in mixed land cover class scenarios.

Second sub task i.e. *sub task 2*, covers successful development and implementation of an optimal class boundary estimator based DT classifier which exploits the PSCFs to achieve improved LC classification. During the development of the optimal class boundary estimator based DT classifier, image and individual class-wise PSCFs statistics are utilized to formulate empirical relationship between classification performance indicator (overall accuracy) and class boundary values. Optimization of this empirical relationship with genetic algorithm resulted in optimal class boundaries which are utilized in a DT to perform LC classification. Classification results using this approach are compared with classification results from RBF-SVM, while keeping the features identical i.e. PSCFs. Classification performance of the proposed approach especially user and producer accuracies for built-up class (72.4% and 71.0% respectively) and
tall veg class (73.8%, and 89.1% respectively) indicate that the proposed approach, i.e. the optimal class boundary estimator based DT classifier, achieved better classification results in mixed LC class scenarios.

In the third sub task i.e. *sub task 3*, development of a two dimensional CNN (2D-CNN) based novel model for LC classification is presented. The 2D-CNNs can directly exploit PSs to perform LC classification, and therefore, the approach reduces the efforts to compute PSCFs. Also, since 2D-CNNs are powerful image extractors, they are able to extract the subtle contextual patterns present in PSs of different LCs. In summary, a novel PSs-CNN LC classification model is realized. Classification results from this PSs-CNN model are compared with NED and NSCM based minimum distance classifiers which are popularly used with PSs for land cover classification. Comparison indicated a superior performance of the novel PSs-CNN model for land cover classification.

Comparison between the two novel approaches indicates that the optimal class boundary estimator based DT classifier is showing an approx. 3% improved classification accuracy than PSs-CNN classifier. However, the PSs-CNN model is easier to implement, does not require an extra step (computing PSCFs), and is scalable.

Overall, the study concludes that; i) PSs have shown significant potential as PolSAR features for land cover applications, ii) segregation of built-up class and tall vegetation class in PolSAR data can be improved by using optimal class boundary estimators with decision trees for classification, and iii) 2D-CNNs have shown potential as classifiers with PSs for LC classification in terms of ease of implementation, and scalability.

**Chapter 4**, presents an efficient approach to map flood affected villages using PolSAR image. The approach included three stages i.e. **first**, built-up area extraction, **second**, village detection, and **third**, flood affected village mapping.

In the first stage, the approach utilized PolSAR polarization signatures (PSs) and CNNs to extract built-up areas from a PolSAR image. A novel PSs-CNN classifier is developed. The PSs-CNN model provided improved built-up area extraction performance in contrast to the NED and the NSCM classifiers. In the second stage, the approach introduced and implemented a novel village detection methodology. The methodology uses a detection filter to detect whether a particular object in the built-up area image (obtained in stage 1) is a village or not. The detection filter uses *apriori* knowledge and image morphology to detect villages in the built-up area image. The detection methodology also significantly removes false alarms and misclassifications. In the third and final stage, flood affected villages are identified and non-flooded villages are masked out using another image morphology based methodology. This methodology computed overlap
ratios between objects (successfully detected as villages) and flooded area and utilized it for identification of flood affected villages.

Quantitative and qualitative assessment of the performance of the novel approach over two different study areas is carried out. Different artificial flood scenarios are created to evaluate the performance of developed approach. The developed approach is evaluated under these flood scenarios and flood affected villages are identified and mapped. Number of villages successfully detected and false alarms are computed with the help of ground truth information (actual number of villages or towns in the study regions are known). Results indicate to high detection accuracy (above 90%), low false alarms and true negatives. The approach is scalable and potentially satisfactory for real time usage.

**Chapter 5** presents a critical analysis of the potential and performance of the one, two, and three dimensional CNNs, where dimensionality refers to the convolved features: spectral, spatial or both. These CNNs are applied to land cover classification with multisensor (PolSAR-MS) satellite image data. Two novel models are introduced, where local filters in either spectral domain (Perm-LS-CNN) or spatial-spectral domains (Perm-LSS-CNN) are applied after a permutation layer. The permutation layer generates an ensemble of band combinations, which are drawn upon by the CNN in order to generate powerful features for land cover classification. Classification results demonstrate the improved generalization ability of these models compared to both alternative CNN architectures and other machine learning models (SVM and random forests). Perm-LSS-CNN also fully uses both PolSAR and multispectral information, improving results compared to other popular methods for PolSAR-MS data based land cover classification.

A separability measure is used for the model significance evaluation. Separability analysis states that the Perm-LSS-CNN transforms the input data space into a feature space where separability among classes increases. This increase in separability is achieved due to the inclusion of permuted local spectral convolution along with the local spatial convolution which is not available in the 1D-CNN-v1, 2D-CNN-v1, or 3D-CNN-v1 CNN models. A fine analysis on ground truth data provides an extra qualitative assessment which complements the quantitative classification results. These demonstrate that the proposed Perm-LSS-CNN model is able to generalize much better while keeping high classification accuracies.

**Chapter 6** presents a comprehensive study and analysis of the performance of conventional CNN and RNN models in MSTS images based on crop classification. The analysis indicated to satisfactory performance of conventional CNN and RNN models in MSTS based crop classification. Also, preferences given to temporal and spatial characteristics of MSTS images by the conventional DNN models is observed. This led to underutilization of spectral
characteristics of MSTS images and therefore resulted in less generalized crop classification models. To address this issue, novel CNN and RNN models are proposed and developed on the concept of local spectral convolutions on ensemble stack of permuted spectral bands of MSTS images. The local spectral convolutions in an ensembled spectral dimension improves the generalization ability of DNN models and also complements the spatial and temporal features. Overall, 12 CNN and RNN models are developed during the study namely 1D-CNN-v1, 3D-CNN-v1, RNN-v1, 1D-CRNN-v1, 2D-CRNN-v1, 3D-CRNN-v1, Perm-1D-CNN-v1, Perm-3D-CNN-v1, Perm-RNN-v1, Perm-1D-CRNN-v1, Perm-2D-CRNN-v1, and Perm-3D-CRNN-v1.

These models are trained on MSTS satellite images of areas having crop fields in challenging landscape. Wheat and sugarcane are the major crops practices in the area considered here for study. The area, apart from croplands, also contain, forests, built-up, grasslands, and deciduous plantations. The study area is a good representation of real world crop classification scenarios particularly in India. The performance of these models in this study area presents be good indicator of their generalization ability.

Quantitative analysis of the classification results from these models indicate to superior performance of the Perm-1D-CNN-v1 model with classification accuracy of 99.7%. However, the Perm-3D-CRNN-v1, which provides 96.00% classification accuracy, indicate to have higher generalization ability during qualitative and visual assessments. The spectral and spatial attributes of land covers (e.g. built-up or popular plantations) is well captured by the Perm-3D-CRNN-v1 whereas the Perm-1D-CNN-v1 appears to be overfitting. Therefore, the Perm-3D-CRNN-v1 is the best model among the classifiers considered.

## Future Scope

The research presented in this thesis has extensive scope and possibilities. A few major possibilities are:

* The PSs-CNN model presented in chapter 3 uses PSs in H-V basis. However, other basis such as in [149] may provide complementary information which can lead to improved land cover classification performance.

* In chapter 4, the approach which utilizes CNNs, *apriori* information, and image morphology to detect human settlements affected by flood disaster has potential to be developed into an operational system.

* In chapter 5, local spectral and local-spectral-spatial convolutions on ensembled input provides better classification results in terms of generalization. However, the ensembled input increases the size of the input which increases the processing time. This increase in the input space can be optimized via more efficient permutation strategies.

* The CNN and RNN models developed for MSTS images based crop classification developed in chapter 6 can also be optimized further in a manner similar to as for models in chapter 5.
A1. Kennaugh Matrix

Kennaugh matrix or K-matrix is the representation of backscattered power from target in terms
of the receiving antenna. The kennaugh matrix represents the received power, given the receiving
antenna polarization characteristics. It is real quantity unlike scattering matrix. It is similar to
Muller matrix but explains for the radar backscattered case instead of optics. Kennaugh matrix
is power matrix rather than amplitude and phase measure in scattering and coherency matrix
respectively. Hence, all element of Kennaugh matrix are real quantities. Given

$$
[S] = \begin{bmatrix} S_{xx} & S_{xy} \\ S_{yx} & S_{yy} \end{bmatrix} \qquad (A.1)
$$

Where, x is the transmit polarization state and y is the receive polarization state. Modified
K-matrix calculated from scattering parameters given in equation (A.1) as follows:

$$
K_{11} = \frac{1}{2} \left[ |S_{xx}|^2 + 2|S_{xy}|^2 + |S_{yy}|^2 \right] \quad (A.2)
$$

$$
K_{12} = K_{21} = \frac{1}{2} \left[ |S_{xx}|^2 - |S_{yy}|^2 \right] \quad (A.3)
$$

$$
K_{13} = K_{31} = \operatorname{Re} \{ S_{xx} * \operatorname{conj}(S_{xy}) + S_{xy} * \operatorname{conj}(S_{yy}) \} \quad (A.4)
$$

$$
K_{14} = K_{41} = \operatorname{Im} \{ S_{xx} * \operatorname{conj}(S_{xy}) + S_{xy} * \operatorname{conj}(S_{yy}) \} \quad (A.5)
$$

$$
K_{22} = \frac{1}{2} \left[ |S_{xx}|^2 - 2|S_{xy}|^2 + |S_{yy}|^2 \right] \quad (A.6)
$$

$$
K_{23} = K_{32} = \operatorname{Re} \{ S_{xx} * \operatorname{conj}(S_{xy}) - S_{xy} * \operatorname{conj}(S_{yy}) \} \quad (A.7)
$$

$$
K_{24} = k_{42} = \operatorname{Im} \{ S_{xx} * \operatorname{conj}(S_{xy}) - S_{xy} * \operatorname{conj}(S_{yy}) \} \quad (A.8)
$$

$$
K_{33} = |S_{xy}|^2 - b + \operatorname{Re}\{S_{xx} * \operatorname{conj}(S_{yy})\} \quad (A.9)
$$

$$
K_{34} = K_{43} = \operatorname{Im}\{S_{xx} * \operatorname{conj}(S_{yy})\} \quad (A.10)
$$

$$
K_{44} = |S_{xy}|^2 - b - \operatorname{Re}\{S_{xx} * \operatorname{conj}(S_{yy})\} \quad (A.11)
$$

Where,

$$
b = \frac{1}{2} \left[ |S_{xx}|^2 + |S_{xy}|^2 \right] - \frac{1}{2} \left[ \left\{ |S_{xx}|^2 + |S_{xy}|^2 \right\} - 4 \left( |S_{xx}|^2 |S_{xy}|^2 - \left\{ (S_{xx} * \text{conj}(S_{xy})) * ((S_{xy} * \text{conj}(S_{xx}))) \right\} \right) \right] \quad (A.12)
$$

The K-matrix is represented as
$$ K = \begin{bmatrix} K_{11} & K_{12} & K_{13} & K_{14} \\ K_{21} & K_{22} & K_{23} & K_{24} \\ K_{31} & K_{32} & K_{33} & K_{34} \\ K_{41} & K_{42} & K_{43} & K_{44} \end{bmatrix} \quad (A.13) $$

This K-matrix is used in the calculation of co- and cross-polarization signatures (refer equation (3.1)).
## Journals

[1] G. S. Phartiyal, N. Bordu, D. Singh, H. Yahia, and K. Daoudi, 'Permuted Spectral and Permuted Spectral-Spatial CNN Models for PolSAR-Multispectral Data based Land Cover Classification', *Int. J. Remote Sens.*, vol. 42, no. 3, pp. 1096-1120, 2020.

[2] G. S. Phartiyal, K. Kumar, and D. Singh, 'An Improved Land Cover Classification Using Polarization Signatures for PALSAR 2 Data', *Adv. Sp. Res.*, vol. 65, no. 11, pp. 2622-2635, Jun. 2020.

[3] G. S. Phartiyal and D. Singh, 'Comparative Study on Deep Neural Network Models for Crop Classification Using Time Series PolSAR and Optical Data', *ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.*, vol. 425, pp. 675-681, Nov. 2018.

## Conferences

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[2] G. S. Phartiyal, N. Brodu, D. Singh, and H. Yahia, 'A mixed spectral and spatial Convolutional Neural Network for Land Cover Classification using SAR and Optical data', in *EGU General Assembly*, 2018, vol. 20, p. 12647.

[3] G. S. Phartiyal, K. Kumar, D. Singh, and K. P. Singh, 'Optimal use of polarimetric signature on PALSAR-2 data for land cover classification', *Int. Geosci. Remote Sens. Symp.*, vol. 2017-July, pp. 4558-4561, 2017.
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