# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 1 Introduction  
Remote sensing satellites acquire useful data for environmental monitoring, such as land cover and land use surveys, urban studies and hazard management, collecting images that provide a global and rapid response of the involved areas [1; 2; 3]. In the traditional processing chain, acquired images are downlinked to the ground for analysis and distribution operations. However, the increasing size of data produced by the Earth observation satellites requires innovative processing techniques. For this reason, intelligent on-board data processing and data-reduction procedures are becoming essential in current and planned missions [4]. In fact, the high-resolution instruments in use produce huge sensor data volumes, which demand high on-board data storage capacity and communication resources, i.e., bandwidth, for data downlinking to end users on the ground. In order to face both the memory and bandwidth availabilities, many future satellite missions are working with the purpose of moving data processing from the ground segment to the space segment through on-board processing operations. In this context, a particular focus regards data management in small satellites. Small satellites have become increasingly popular due to their small size, low power consumption, short development cycle and low cost. However, they are subject to limited on-board transmitting power and limited communication band choices, as well as restricted storage space, so a new processing flow is required rather than the traditional one [5]. Therefore, having the possibility to directly deliver the concerned higher-level information contained in the remote sensing images becomes crucial; this is indeed usually many orders of magnitude smaller than the remotesensing data themselves. Moreover, the processed results will greatly improve the downlink data transmission efficiency, allowing for reduction in the transmission bandwidth and enhancing the transmission rate. Additionally, by selecting only the useful information, the on-board platform memory cost can be reduced [6; 7]. On-board processing requires several characteristics, such as flexibility, robustness, low computation burden. For this reason and also because of their ability to deal with complex data, such as remote sensing data, artificial intelligence models such as neural networks (NNs) represent a valid instrument [8; 9]. NN algorithms are characterized by a collection of linked units that are aggregated in layers; the strength of each connection is adjusted by specific weights, or the coefficients of the model, which are determined during the NN learning process. The learning phase represents the most expensive task, but this can be performed off-line, with the ground stations capabilities, without affecting on-board processing operations. Once the coefficients are known, the model, if not too demanding in terms of storage, can be implemented on-board for the desired analysis. In this context, the main interest is in auto-associative neural networks (AANNs). AANNs are symmetrical networks in which the targets used to train the network are simply the input vectors themselves so that the network is attempting to map each input vector into itself. The middle layer is a bottleneck layer that forces a compressed knowledge representation of the original input [10]. For their characteristics, AANNs have already been used to perform dimensionality reduction and to extract relevant features from remote sensing images [9; 11; 12]. Moreover, AANNs, which are shallow networks, require low-level computational resources; thus, they are particularly suited for on-board operations. In this work, the AANN is used to develop a low computational resource algorithm able to detect changes occurring between two images taken over the same area at different times. In remote sensing, change detection analysis is an important application that allows users to monitor phenomena such as urbanization, land cover change and natural disaster [13].