Multi spectral, Remotely Sensed data compression method using neural networks
Junichi Suzaki
Institute of Industrial Science, University of Tokyo,
7-22-1 Roppongi, Monato-ku, Tokyo, Japan
Eihan Shimizu
Department of Civil Engineering, University of Tokyo,
7-3-1 Hongo, Bujkyo-ku, Tokyo, Japan
Abstract
In this research, we examined the feasibility of the use of Neural Networks (NN) for the compression of multi spectral Remote Sensing data. NN is used to reduce the dimensions of original data meanwhile keeping the information content as highly as possible. Firstly, we analyzed the theoretical background of NN in data compression and its relative merits with principal component analysis (pca) which is traditionally used for compressing huge data sets. Then, we compared an image using data compressed by NN with a natural color image . It was found that the image generated from the NN compressed data is more suitable for visual interpretation , because it contains more characteristic ics (information) of the original data.
Comparisons of both 1) the NN model with pca , and 2 ) an image created from NN compressed data with a natural color image show that the NN can be used successfully as data compression method.
1. Introdution
Multi spectral Remote Sensing data can be used for land classification. Two types of classification techniques exist: automated classification and visual interpretation. Visual interpretation is usually carried out based on a false color image generated by using three channels of multi spectral data. In general, multi spectral data contains more channels that is required for the false color image, and it results unused information (channels) of the original data. We discussed a method to generate an image which is capable to reflect all the channels of data. It requires the compression of multi channels into three channels. So far, there are a lot of researches about compression, most of which deal with pca. Some researches show that pca is useful to reduce the number of dimensions and to get new independent data meanwhile reflecting all the dimensions of original data. Contrary, others show that it is not so effective because of its restraint linear transformation.
In the study, NN models, a widely used data processing method, was applied because they are flexible to even non-linear data. First of all, a NN model suitable for data compression was constructed. Then, it was applied for Remotely Sensed data and the result was compared with pca's. Finally, we validated it as a data compression method by comparing an image compressed by NN with an original image.
2. Data Compression by NN
Multi-layered NN models have been widely used among NN models. The three-layered model, the simplest one for data compression, is shown in figure 1.
Figure 1 Identity mapping NN model for data-dimensional compression
It has as many units in the input layer as in the output layer, while it has less ones in the hidden layer. Input data are also used as training data and the model continues to modify both weight and offset parameters so that output data are closer to training data. This means that such models (e.g. presented on Figure 1) represent identity mapping.
But Funahashi1) proved that data recompressed by three-layered model (e.g. Figure 1) are inferior to ones recompressed by pca.
On the other hand, Irie2) showed that layered NN, whose units in the hidden layer are mote than ones in both the input layer are more than ones in both the input layer and the output layer (Figure 2), can be adaptive for almost all non-linear data set.
Figure 2 NN Model adaptive for non-linear data set
Thus we constructed a model, shown in Figure 3, by combining a model described in Figure 2 with a model linearly symmetric to it.
Figure 3 NN model for data-dimensional compression
This leads that the first three layers of the model functions to compress, while the last three layers functions to recompress data. We can use data of the second hidden layer as compressed data which should be superior to ones compressed by pca and has to reflect all channels of data.
