Q&A

What is a sensor in the context of remote sensing?

A sensor is a transducer that transforms energy from one domain to another, making it accessible for data collection. In current remote sensing practice, most imaging sensors are based on arrays or matrices of cells that convert photons with energy into electrons.

How do imaging sensors work based on converting photons to electrons?

These sensors utilise arrays of cells that function as "wells" or "buckets." When a photon strikes a cell with sufficient energy, it can release an electron. The probability of transforming a photon into a free electron is called quantum efficiency. The collected electrons within these cells over an integration time represent the number of photons captured, and the voltage over the cell's capacitor serves as a measure of the number of photons.

Why do thermal infrared sensors require cooling?

Thermal infrared sensors face the challenge of distinguishing photon-generated electrons from electrons generated by the sensor elements themselves due to heat. Cooling the sensors helps to reduce this background noise and improve the accuracy of measurements.

How do microwave sensors detect radiation?

In the microwave domain, the energy per photon is too low to generate free electrons directly. Instead, microwave sensors use the electrical field of microwave photons to increase the voltage and current variation of an electromagnetic resonator.

What is the significance of resonance in remote sensing applications?

Resonance is important because it describes how systems respond to oscillating energy. In photon-electron interaction, electrons are pushed to higher energy levels if their dynamic energy resonates with the dynamic energy of the photon. In microwave and radio wave detection, using the same frequency as a dipole antenna's natural frequency allows enough amplitude to build up to be detected.

How are photons sorted or "filtered" based on their properties?

Photons can be sorted or filtered using selective resonance absorption filters, as seen in RGB (red, green, blue) and infrared cameras. These filters allow transmission in specific frequency bands while blocking others. Interference filters, based on making resonance paths using transparent layers, can also be used to pass a small range of wavelengths or equivalent frequencies.

What is the relationship between measurement, information, and knowledge in remote sensing?

Measurement is the interaction between two objects in space-time that produces data about that interaction. Information is a statistical relationship between possible questions and answers, requiring data and the interaction models used to obtain the data. Knowledge is based on hypotheses and measured evidence, often expressed as the probability of a hypothesis given the evidence. Knowledge about objects derived from photon sensors can be based on the frequency of coincidence statistics of controlled experiments, where hypotheses are preferably formulated as prediction models for spatial and spectral features.

How is the truth value of a classification determined?

The truth value of a classification (which represents discrete class hypotheses) is reported by the frequencies of proper and misclassified data samples. This assumes the experimenter has experimentally determined the frequency of evidence for each class. The relationship between the probability of evidence given a class and the frequency of evidence given a class from a controlled, supervised measurement provides the knowledge. The given data set is then classified, and for each data point, the likelihood vector is updated to the frequency average. This can be mapped to maximum likelihood, producing a binary approximation of probabilistic truth, like class membership.