## 1 Introduction  
Rapid growth in remote sensing (RS) technologies and deployed satellite missions has led to substantially large remote sensing imagery datasets. A single remote sensing mission can have multiple different apparatuses that simultaneously and continuously collect data. For example, the Sentinel 2 satellite missions are comprised of two launched satellites (Sentinel 2A and 2B) collecting multi-spectral images, with the latter producing 1.6 terabytes of data per day. The diversity and complexity of RS data give validity and definition to the RS big data problem [1]. Many techniques used to explore and categorise these images for downstream analysis require previously labelled samples to be able to train and test algorithms on unseen imagery. Increasing the volume and diversity of labelled samples gives more confidence that we can attribute to any given algorithm as it has been tested on a more diverse sample range. However, acquiring a sufficiently large and accurately labelled dataset of images for any given RS technology and technique requires immense effort from an RS expert analyst [2]. Ultimately, this results in a significant problem of sorting data of interest from the ever-growing volume and diversity of image datasets. Producing labelled images for analysis requires a large effort from remote sensing specialists when considering temporal differences for one location, which is then compounded by involving multiple locations. Providing context to images by binning them into categories or labels from a human perspective is to understand the physical matter and contextual arrangement of pixels within an image. The physical matter of any individual pixel can be discerned by the reflected wavelengths of light detected by the studied imaging platform, e.g., for a red green and blue image, plant matter is green. Increasing the diversity of detected wavelengths from a simple RGB composite image can improve our understanding of materials, which is why modern satellite missions include infrared detection capabilities facilitating the use of hand-crafted feature extraction methods such as Normalized Difference Water Index (NDWI) [3] and Normalized Difference Vegetation Index (NDVI) [4]. However, such techniques ignore the geographical arrangement and contextual information within an image, as certain features are often located near each other, e.g., buildings and roads, mountains and tributaries, etc. [5]. Images that have the same physical matter or arrangement similar to a known sample will have a similar value from a hand-crafted feature. To discern whether the score is similar or not, techniques like K-means clustering, SVM, LDA or GMM's are utilised [6; 7]. Expanding these techniques to accommodate unseen samples or non-trivial problems could require clustering all old and new samples again, in addition to redefining the hand-crafted features to include the new subtleties within the data [8]. These hand-crafted features have in recent years been replaced by the use of deep convolutional neural network (DCNN) techniques. A DCNN produces optimised filters for the analysis of images based on the textures present creating complex non-linear representations. Building on Stacked Auto Encoders (SAE) [9] convolutional autoencoders add spatial learning abilities by introducing learnt filters and activation maps, allowing the model to learn local texture representations and inter-channel information. This architectural design for feature representation has demonstrated considerable success in remote sensing, as evidenced by various studies [10; 11; 12; 13]. There are many variants of AEs that have also been utilised, such as Variational AEs (VAEs), that replace the latent space with a distribution instead that have been used in, for example, image captioning [14], desertification [15] or biomass prediction [16]. Querying images for similarity can be seen in the field of Image Retrieval (IR) within the RS field [17].