ICA Neural net to refine remote sensing with multiple labels

We show how the unsupervised Artificial Neural Network (ANN) modeling of image fusion of Human Visual Systems (HVS) can embody the mathematics of Independent Components Analyses (ICA) to achieve blind source de-mixing of remote sensing images. We have shown when two eyes are extended to multiple eyes for multiple spectral looks, we refine the pixel resolution of remote sensing with Multiple-Labels-Remote-Sensing (MLRS). This percentage decomposition capability is desirable because a satellite image pixel has a large footprint on the ground (about 30 meters by 30 meters in NASA Landsat, and 1.1 km by 1.1 km in NOAA weather satellite). MLRS gives the percentage composition of ground radiation sources within the footprint and thus overcome the so-called boundary error coined by Tucker in the Amazon deforestation over-estimation as follows. This classifier error may be caused by the traditional winner-take-all (WTA) voting strategy-- a single label per pixel based on an object-spectral lookup table from the outer space. Although the WTA methodology has been satisfactory in classifying large landscape, the remote sensing is not accurate enough to enforce the forestland utilization taxation law of Amazon and end up with a very costly ground surveys in the jungle. The percentage error e(x,y), when integrated over all the trees existed at the boundary pixels (x,y) between the so-called farm and the forestland pixels, grows linearly over the size of boundary and becomes particularly significant when the forest has been massively cut by bulldozers into road branches like a fishbone in a bird's eye view. One needs MLRS, which yields the percentage composition of earth canopy within the pixel footprint. If we assume that the radiation sources are independent of one another, and the data are linearly mixed instantaneously, then the ICA model matches perfectly the MLRS model and we can find truly independent ground composition beyond the capability of the second order covariance matrix (PCA methodology). Thereby, the ICA that mathematically factorizes the joint probability density & predicts likewise the decomposition of radiation sources in a thermally equilibrium. The unique association between the radiation sources in equilibrium and the actual ground objects remains to be benchmarked. However, the preliminary result indicates affirmatively that given the Landsat image in the Mediterranean (littoral) environment the unexpected ground truth and sensor noise reveal themselves in ICA but not in PCA. This ICA model embodying the massively parallel ANN (e.g. by FPGA firmware) should be quite attractive for real time & in situ implementation on board of a satellite or airplane. HVS extracts effortlessly the singularity map (SM) by means of the Lateral Inhibition Mechanism for the redundancy Reduction (RR) principle without using the differential calculus. Moreover, it tracks easily the SM in time and solves the correspondence problem of the optical flow of SM (not so easily if using pixel map). The review points to the future where a tremendous redundancy reduction in the scientific data is anticipated, e.g. the several hundred channels of hyper-spectra data basis, for useful data mining for the information retrieval and the knowledge understanding, in order to tackle head-on the data explosion hyper-spectral era.