Performance evaluation of dimensionality reduction techniques on hyperspectral data for mineral exploration

With recent advances in hardware and wide range of applications, hyperspectral remote sensing proves to be a promising technology for analysing terrain. However, the sheer volume of bands, strong inter band correlation and redundant information makes interpretation of hyperspectral data a tedious task. Aforementioned issues can be addressed to a considerable extent by reducing the dimensionality of hyperspectral data. Though plethora of algorithms exist to downsize hyperspectral data, quality assessment of these techniques remains unanswered. Since Dimensionality Reduction (DR) is a special case of unsupervised learning, classification accuracy cannot be directly used to compare the performance of different dimensionality reduction techniques. As a consequence, a different type of goodness measure is essential which is expected to be easily interpretable, robust against outliers and applicable to most algorithms and datasets. In this paper, fifteen popular dimensionality reduction algorithms are reviewed, evaluated and compared on hyperspectral dataset for mineral exploration. The performance of various DR algorithms is tested on hyperspectral mineral data since the extensive study of DR for mineral mapping is scarce compared to land cover mapping. Also, DR techniques are evaluated based on coranking criteria which is independent of label information. This facilitates to demonstrate the robust technique for mineral mapping and also provides meaningful insight into topology preservation. These techniques play a vital role in mineral exploration since in field observation is expensive, time consuming and requires more man power. From experimental results it is evident that, deep autoencoders provide better embedding with a quality index value of 0.9938, when K = 120 compared to other existing nonlinear techniques. The conclusions presented are unique since previous studies have not evaluated the results qualitatively and comparison between conventional machine learning and deep learning algorithms is limited.