Multi-label sub-pixel classification of red and black soil over sparse vegetative areas using AVIRIS-NG airborne hyperspectral image

The limited spatial resolution of the hyperspectral (Hx) images corrupts the spectral information of pure materials and their distribution in an image. The accuracy of characterising or classifying the soil using Hx or Mx images decreases when surfaces are covered by vegetation. In the presence of vegetation, a single pixel can be labelled as either vegetation or a specific soil type. In this context, we have studied the usefulness of the multi-label classification (MLC) approach to classify the soil colour in the presence of vegetation cover. We have evaluated its performance on airborne Hx (Airborne Visible InfraRed Imaging Spectrometer -Next Generation, AVIRIS-NG) images acquired over Berambadi catchment, Karnataka, India. The potential of MLC to classify soil types using simulated Sentinel-2 images (Sen-S) was also explored in this study. The surface soil colour in the Berambadi catchment was classified into two soil types ("black"and "red"soils). The proposed MLC approach consists of (1) simulating the mixed spectra of vegetation, red soil, black soil and non-photosynthesis-vegetation (NPV) using linear-mixture-model (LMM) and bi-linear-mixture-model (BLM) to generate a well-balanced calibration data set, and (2) labelling of each pixel into multiple classes using MLC approaches. Performances of classical and deep-neural-network (DNN) based MLC models were compared to identify the best performing model. Our results showed significant performance for the cost-sensitive-multi-label-embedding (CLEMS) model when applied to both AVIRIS-NG (OA=97%) and Sen-S (OA=93%) images. The proposed method requires a limited number of ground-truth samples, and it is operationally practical for large Hx and Mx images.