Hyperspectral imaging can capture hundreds of images acquired for narrow and continuous spectral bands across the electromagnetic spectrum. Since the spectral profiles are specific for different materials, exploiting such high-dimensional data can help determine the characteristics of the objects of interest that may be not possible to spot by the naked eye. A hyperspectral image can be interpreted as a data cube which couples spatial and spectral information captured for every pixel in the scene. Practical applications of such imagery are very vast and spread across a variety of fields, including, among others, biology, medicine, forensics, precision agriculture, and remote sensing. High dimensionality and volume of hyperspectral data significantly affect the cost and time of transferring such images and make them challenging to analyze and interpret manually. Thus, there are a plethora of state-of-the-art approaches toward automating the hyperspectral data analysis process, and they benefit from a wide spectrum of machine learning, computer vision, and advanced data analysis techniques. However, the availability of manually-annotated hyperspectral datasets is still limited, and they are often small, not very representative, extremely imbalanced, and noisy, e.g., due to the noise that is intrinsic to the data acquisition itself, especially in the context of satellite imaging. These issues make the supervised machine learning-powered algorithms challenging to apply in emerging multi/hyperspectral image analysis scenarios.
In this talk, we will focus on estimating the soil moisture (in the context of the potato production) from hyperspectral data using both classical machine learning and deep learning techniques (the former require building feature extractors commonly followed by feature selection, whereas the latter utilize automated representation learning). Soil moisture is an important parameter, and its precise estimation can help us effectively control the amount of water in the field for a variety of precision farming applications, but its in-situ analysis is cumbersome and not scalable for large agricultural area. Therefore, exploiting the recent advances in the artificial intelligence area may significantly accelerate the process of estimating this soil parameter (and also other important parameters of soil) in a non-invasive and inherently scalable manner, e.g., if the hyperspectral data is acquired on-board an imaging satellite. We will discuss both classical machine learning and deep learning approaches toward elaborating this soil parameter, and present the experimental results obtained for the real-life hyperspectral image data that was coupled with the in-situ ground-truth information (acquired in Poland). It is worth noting that – in the case of hyperspectral imaging – high-dimensional image data is commonly captured for many bands acquired for different wavelengths. Therefore, transferring, storing, and analyzing such images is expensive due to their volume, especially if they are acquired on-board an imaging satellite. To this end, we will discuss our approaches for reducing the dimensionality of such data, also using deep learning algorithms equipped with the attention modules. Finally, we will discuss our thorough quantitative, qualitative, and statistical validation procedures, and show why the validation of the artificial intelligence-powered techniques is pivotal in practical Earth observation applications. The talk will be concluded with the review of the practical challenges that need to be faced while deploying machine learning (especially deep learning) algorithms on-board a satellite in a very resource-constrained and extreme execution environment – we will focus on our Intuition-1 satellite which is a 6U-class satellite with a data processing unit enabling on-board data processing acquired via a hyperspectral instrument currently being developed by KP Labs (will be launched in Q1 2023).