Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

The accuracy and rapidity of total iron content (TFE) analysis can accelerate ore production. Although the conventional TFE detection methods are accurate, its speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method based on reflectance spectroscopy (wavelength range: 340–2500 nm) remote sensing. Firstly, spectral experiments were conducted using HR SVC-1024 spectrometer to obtain data for each sample. Then, spectra smoothed dimensionally reduced by wavelet transform principal component analysis. To improve TFE, two hidden layer extreme learning machine with variable neuron nodes an improved sparrow search algorithm batch normalization optimization (MSSA-BNVTELM) is proposed. According experimental results, MSSA-BNVTELM exhibited superior comparison other algorithms. In addition, research established sensing model Sentinel-2 MSSA-BNVTEM detect distribution mining area. mine area was plotted results. results show that be useful distribution, providing assistance plan.