Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed’s spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm’s optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF.

Wide mode SAR images have an apparent incidence angle effect. The existing incident angle normalization methods assume that the relationship between the incident angle (θ) and the backscattering coefficient (σ_PQ) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of σ_PQ based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in θ-σ_PQ relationship was investigated based on time-series (April to October) σ_PQ of maize canopies in northeast China, and a dynamic method based on normalized difference vegetation index (NDVI) was developed to normalize the effect of θ on σ_PQ. Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band σ_PQ on θ varies with maize NDVI. In addition, the value of N changed from 9.35 to 0.66 at VV polarization from bare soil to biomass peak, and from 6.26 to 0.99 at VH polarization; (2) a dynamic method was proposed to quantify the change of N based on its strong correlation with NDVI, indicated by R² of 0.82 and 0.80 for VV and VH polarization, respectively; and (3) the overall root mean square error of normalized σ_PQ based on the newly-developed dynamic method is 0.51 dB, and this accuracy outperforms the original first-order cosine method (1.37 dB) and cosine square law method (1.08 dB) by about 63% and 53% on the whole. This study provides a dynamic framework for normalizing radar backscatter coefficient, improving the retrieval accuracy of land surface parameters from radar remote sensing.

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary’s clarity. This study provides a new idea for refined image processing.

A digital automatic gain compensation (AGC) drone-borne K-band microwave radiometer with continuous high-speed acquisition and fast storage functions is designed and applied to obtain high-resolution radiation brightness temperature (TB) images. In this paper, the composition of the drone-borne passive microwave observation system is introduced, a data processing method considering the topography and angle correction is proposed, the error analysis of the projection process is carried out, and finally, a high-resolution microwave radiation TB image is obtained by a demonstration area experiment. The characteristics of the radiometer are tested by experiments, and the standard deviation of the TB is 1K. The data processing method proposed is verified using a demonstration case. The corrected data have a good correlation with the theoretical values, of which the R² is 0.87. A high-resolution radiation TB image is obtained, and the results show the TB characteristics of different objects well. The boundary of the ground object is closer to the real value after correction.

Marius Hills volcanic complex (MHVC) comprises one of the densest volcanic structures on the Moon and is of significant interest for comprehending lunar volcanism. In this study, brightness temperature (TB), normalized brightness temperature (nTB), and brightness temperature difference (dTB) were first generated based on microwave radiometer (MRM) data from the ChangE-2 Lunar Orbiter and (FeO+TiO2) abundance was calculated from Clementine UV-VIS data to investigate the microwave thermal emissions and evolutionary history of the MHVC. The results are as follows. Firstly, surface composition derived from the optical data is strongly influenced by impact events, and MRM data can better reflect the thermophysical properties of surface deposits. Secondly, a notable concentration of high TB values at daytime suggests an enrichment of volcanic activities. Thirdly, later volcanic activities are indicated by the TB performances at daytime and night in multiple regions. Finally, a hot anomaly was detected by MRM data, suggesting the higher substrate temperature and the existence of a potential hidden volcanic structure. The results suggest that MRM data provide new insights into the evolutionary history of the MHVC, as well as an important perspective for lunar thermal evolution.