# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 1 Introduction
NNs are one of the methods used to perform change detection, along with other approaches that include mathematical operations, principal component analysis (PCA), and geographic information systems (GIS) [14; 15]. Particularly, in [16], NNs with an encoding-decoding structure were used to obtain an encoded representation of the original multi-temporal image pair. Then, image subtraction was performed to detect seasonal and permanent changes that occurred over the time period. Additionally, in [17], a deep NN model was used to extract image features that were later used to build the input dataset for another NN trained to classify between changed and unchanged images. In this context, on-board change detection introduces two interesting advantages: it reduces the amount of data transmitted to the ground by sending only the changed images, and it provides products for rapid decision-making. Some examples regarding advanced on-board processing operations, mainly based on NN algorithms, can be found in the literature: [PERSON] et al. proposed an on-board ship detection scheme to achieve near real-time on-board processing by small satellites computing platform [18], while [PERSON] et al. suggested a similar technique for cloud detection [19]. In [20], [PERSON] et al. implemented an algorithm based on convolutional neural networks for volcanic eruptions detection based on Sentinel-2 and Landsat-7 optical data, while [PERSON] et al. used SAR images to identify oil spills through a CNN algorithm [21]. A procedure based on self-organizing maps is proposed in [22] by [PERSON] et al. to cluster hyperspectral images on-board, and, in [23], [PERSON] et al. successfully designed a hardware accelerator based on field programmable gate arrays (FPGAs) specifically made for the space environment. Despite this, and although change detection applications in the ground segment are very important and largely investigated [24], literature regarding on-board change detection is still very sparse and limited. When this paper was written, the only significant reference found was the work of [PERSON] and [PERSON] [25], who proposed a change detection-classification module specific to aircrafts based on very high spatial resolution (0.2 m and finer) SAR (synthetic aperture radar) and optical aerial images. The detection of changeswas basically performed relying on the computation of a correlation coefficient, and NNs were merely used to classify the detected changes (e.g., plane-not plane). Next, they applied a compression ratio to uninteresting image sections to reduce the amount of data transmitted to the ground. The change detection procedure here proposed aims at detecting whatever alteration eventually occurred by inspecting a representation of the input image pairs in a reduced space, which is obtained by means of an AANN. This method is considered suitable for the on-board execution since it does not require high computational resources. In fact, power consumption of the algorithm, detailed and described at the end of Section 3, is aligned with the resources that might be available on-board [26]. After the learning phase is completed, the core of the procedure is to encode the input image patches through the AANN at time t\({}_{1}\) and time t\({}_{2}\) into smaller representative vectors. Then, a dissimilarity measured is performed, which drives the final decision between change or no change. The dataset used in this paper focuses on Sentinel-2 satellite data because, besides being freely available, they are characterized by high spatial resolution and revisit time, which make them a good test for the images that will be provided by upcoming satellite missions with similar characteristics [27; 28]. Another technique is also considered in this study, which plays the role of a benchmark for the performance analysis of the presented methodology. In fact, the results obtained with the AANN compression have been compared with those obtained with a discrete wavelet transform (DWT) compression [29].