Abstract: Systems and techniques are provided for compressing data. A process can include generating a compressed sub-packet by removing one or more sparsity bytes from a sequence of values corresponding to a sub-packet, the sequence of values including one or more sparsity bytes each equal to a configured sparsity value and one or more non-sparsity bytes each corresponding to a respective data value different from the configured sparsity value. A sub-packet header can be generated for the compressed sub-packet, and indicative of a respective location within the sequence of values of each non-sparsity byte. A packet header can be generated for a plurality of compressed sub-packets, and indicative of the configured sparsity value and respective coding information for each compressed sub-packet. A compressed data packet can be generated to include at least the packet header, the sub-packet header, and the one or more non-sparsity bytes included in the sequence of values.

Abstract: A remote sensing assessment method for soil erosion control degree based on maximum erosion potential includes: data preparation and process, and determining a current soil erosion modulus, a maximum possible soil erosion modulus, and a soil erosion control degree. A soil erosion modulus is determined as the maximum possible soil erosion modulus of a research area under a condition that a most serious water and soil loss possibly occurs; the maximum possible and the current soil erosion moduli are calculated by using a USLE model; and a ratio of a difference between the maximum possible and the current soil erosion moduli to the maximum possible soil erosion modulus is determined as the soil erosion control degree. Therefore, the soil erosion control degree is representative and popularized to measure soil management potential, which improves evaluation efficiency thereof and provides an important reference for further adjusting soil erosion management measures.

Abstract: A remote sensing classification method based on relative entropy includes: determining sample points of n types of ground objects in a study area and determining series remote sensing parameters; extracting, based on the sample points, remote sensing parameter values to form standard time series plots as a first distribution; taking remote sensing parameter values of to-be-classified pixels as a second distribution, determining, based on the second distribution and the first distribution, KL values of the to-be-classified pixels by using a KL-divergence formula, then obtaining n KL layers; and comparing n KL values of each to-be-classified pixel to classify it to be a type of ground objects with a minimum KL value.