In this paper, we present a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also propose a composite fusion architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results.

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The qualitative determination of acetone is performed by passive Fourier transform infrared (FT-IR) spectrometry with data spanning two spectrometers. Digital filtering and piecewise linear discriminant analysis techniques are optimized and applied directly to short interferogram segments to eliminate background and instrument variation and then perform pattern recognition. Once optimized, this methodology classifies remote sensing data into categories representing the presence or absence of the analyte in an automated fashion. The addition to the training set of small numbers of interferogram data from a second spectrometer is evaluated in the creation of qualitative models robust with respect to differences between the instruments. Results of these experiments show that classification percentages averaged across all tested interferogram segments are improved from 76.2 ± 6.4% to 95.1 ± 2.2% with the addition of as few as 10 background interferograms collected in the field with the secondary instrument. The results also demonstrate that a broader, more optimal range of segments in the interferogram can be utilized when these background data are added from the secondary instrument. It is also found possible to standardize the data from the secondary instrument with blackbody background interferograms collected in the laboratory.