Total suspended solids (TSS) represent a critical parameter for water quality assessment and an essential indicator for remote sensing water quality monitoring. The Gaofen (GF) series satellites are a crucial component of China's “Major Special Project of High Resolution Earth Observation System”, widely applied in fields like environmental protection and land resources monitoring. In our efforts to further explore the applicability of the satellite network composed of GF-1 and GF-1B for TSS change monitoring, we developed a TSS concentration model for the coastal waters south of Yantai, China, and discussed the impact of different aerosol models on inversion results. Our findings indicate that the optimal band combination for TSS inversion modeling in the coastal waters south of Yantai is "ln(B3)-ln(B2)". The model best suited for TSS concentration inversion in the study area is y = 9.9348x2 + 29.713x + 23.879, with a coefficient of determination (R^2) of 0.73. The choice of aerosol model when employing the FLAASH model for atmospheric correction influences the predictive accuracy of the inversion model for TSS concentration. GF-1 and GF-1B satellite data are promising for inverting TSS concentrations in water bodies, and high temporal resolution satellite constellation images are beneficial for enhancing regional water body environmental monitoring.
It is difficult to achieve detailed segmentation since the building size varies in high-resolution remote sensing images, especially for small buildings. To address these problems, a dense feature pyramid fusion deep network is proposed in this study. First, we built an encoder-decoder structure, and combine attention mechanism and atrous convolution to improve the feature extraction results in the encoder. Second, the pyramid pooling module is selected to extract the multi-scale features from different levels. Finally, dense feature pyramid is adopted in the decoder to fuse multi-level and multi-scale features to obtain the final segmentation results. Experiments on Inria Aerial Image Labeling Dataset show that our method achieves competitive performance compared with other classical semantic segmentation networks.
Drill core is very important for researchers to study lithospheric structure. However, how to save cores in good condition as well as how to satisfy the need of studying drill cores anytime and anywhere as we want are big questions. Thus, numerical images of drill cores are very necessary. Hyperspectral images have lots of advantages, for they can provide both true color images and hyperspectral messages. True color images can be used for direct observation and study. And hyperspectral messages can be used for mineral identification. In practical use, there are distortions in the scanned hyperspectral images, so correction method is in great need. A geometric and luminance correction method is raised in this paper, and results show that the correction method works well. For the sake of observation, three-dimension display is more convenient. This paper provides an implementation method of 3D display. At last, this paper provides a method for extracting mineral alteration data. Through the methods above, hyperspectral images are useful and feasible in the digital record of drill cores.
Hyperspectral HyMap image with synchronous in-situ spectral data were used to survey the environmental condition in Shenzhen of South China. HyMap image was measured with 3.5m spatial resolution and 15nm spectral resolution from 0.44μm-2.5μm and corrected with Modtran5 model and synchronous solar illuminance and atmospheric visibility to the ground. The spectra of rocks, soils, water and vegetation were obtained by ASD spectrometer in reflectance. Both the fresh granite and eroded sandy soil was found with absorption at 2200nm±in-situ spectra, but the weathered granite and sandy soil have another absorption at 880nm~940 nm. Polluted water with high ammonia nitrogen and phosphorous and BOD5 get the strongest reflectance at 550 ~570nm, while polluted water of high CODcr and heavy metal ions content get the peak reflectance at 450~490nm. The in-situ spectra was resampled in wavelength range and spectral resolution to that of Hymap sensor for image classification with SAM algorithm, the unpaved granite among cement the paved mine pits , the newly excavated land surface and the eroded soil was mapped out with the accuracy over 95%. We also discriminate the artificial forest from the natural with the spectral endmember extracted from the image.
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