Thermal infrared (TIR) remote sensing is an important technology for detecting geothermal anomalies. However, detection results have been found to have a certain dependency on the distribution of ground objects and imaging conditions at different times, and pseudo-anomalous areas are easily extracted. To solve this problem, a new geothermal anomaly detection method is proposed in this paper and implemented in the Jiaonan uplift in the Yishu fault zone. A temperature inversion experiment is carried out on TIR remote sensing images based on the radiation transfer equation. Then, a gradient operator is used to extract high-temperature regions in various periods, and geothermal anomaly areas are selected through the spatiotemporal analysis method proposed in this paper after excluding the influence of impervious surfaces, water bodies and vegetation. The temperature anomaly points which are all high-temperature points in each inversion result and geothermal anomaly areas extracted by the proposed method are compared with the five geothermal anomaly points, which are determined based on 150 geothermal wells and the geological structure in the study area. The spatial locations of the temperature anomaly points and geothermal anomaly areas are close to those of the geothermal anomaly points. Compared with the mean grad method, the proposed method is found to effectively delete some pseudoanomaly areas under the premise of ensuring the extraction accuracy of geothermal anomaly areas. Index Terms-Geothermal anomaly; thermal infrared remote sensing data; temperature inversion.

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With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.

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