# Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (C.D. ); [EMAIL_ADDRESS] (Y.C.) College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 1  
[PERSON]  
1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (C.D. ); [EMAIL_ADDRESS] (Y.C.) College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 12222  
the available literature confirms that there are several statistical techniques able to determine the relationship between the estimated reflectance and physicochemical parameters of the water sample. In fact, several models were developed to investigate the relationship between measured values of COD in the laboratory and remote sensing reflectance, based on establishing linear, exponential or logarithmic regressions. Recent studies that estimate physicochemical parameters of water using such relationships are cited in Table 1.  
Although their results indicate that Landsat/Thematic Mapper (TM) imagery was used more than other sensors to estimate COD of waters, it has a relatively low potential and low accuracy, compared with other remote sensing techniques for the measurement of COD values in water bodies. Clearly, the relationships between spectral characteristics of images and in situ measurements of COD in the water bodies are still poorly understood. There also are some technical difficulties involved in analyzing the water absorption spectrum. For instance, there is mutual interference and cross sensitivity among different chemical substances, leading to errors. It was found that some of these issues could be resolved by using the capsule network model. The capsule network is a deep learning model recently introduced by [PERSON] in December 2017 [23], which is original designed for a graph classification mission. It represents a completely novel type of neural network architecture that attempts to overcome the limits and drawbacks of CNN, such as lacking the explicit notion of the entity and losing valuable information during pooling layer. A capsule is a group of neurons that represents the instantiation parameters of an entity in the input data, while the length of the capsule represents the probability that the entity exists in the dataset. The capsule network has achieved good performance (5% error rate) on the task of segmenting and highly overlapping digits on MNIST dataset, which presents in Figure 1. However, this task could not be done with convolution neural networks (CNN) [24]. The authors were inspired by this achievement and applied the capsule network on a water spectrum analysis, since the water body contains different chemical substances and each substance has its absorption spectrum. The overall spectrum is the collection of all its substances spectrum that share a similar circumstance with highly overlapping digits. Moreover, this similarity can be further extended to many other remote sensing analyses because the spectrum usually represents the various properties of a particular entity that is present in the data. These properties can include many different types of instantiation parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor** & **Reference** \\ \hline Landsat 5-TM & [12; 13; 14; 15] \\ Landsat 5-MSS & [16] \\ WorldView-2 & [17] \\ IRS-LISS-III & [18] \\ MODIS & [19] \\ MERIS & [19] \\ AVHRR & [20] \\ SeaWiFS & [21] \\ SPOT & [22] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review of the estimation of physicochemical parameters of water using a remote sensor. An interesting part of the capsule network is that it can reconstruct an input by using the outputs of the capsule vectors through the encoder-decoder method.