Imagery from the thermal infrared spectrometer (TIS) of the first sustainable development goals science Satellite (SDGSAT-1) provides a high-resolution observation view of ground objects, while the nonlinear response between imaging modules affects the quality of the image. Using the thermal infrared data from SDGSAT-1 TIS, an effective method of intermodule nonlinear response correction is presented in this letter. First, an improved frequency domain guided LRSID algorithm is utilized to eliminate the fringe noise in the module, in conjunction with a boundary condition to avoid distortion at the boundary of the module. Next, a nonlinear response correction method utilizing a fully connected neural networks is developed to compensate for the difference between modules based on overlapping pixels. The correction parameters are calculated by the network trained on overlapping pixels from noiseless images. Results show that this method has produced considerable improvements in visual effects, and the distortion in response between modules was controlled below 0.5%, which is comparable with the work for Landsat 8. Besides, the edge slope (ES) of the corrected image makes almost no reduction, which means the nonlinear response is corrected without sacrificing image quality.

In the on-orbit images of the SDGSAT-1 TIS, there are image striping phenomena caused by the nonlinear response of the detector and flickering effects caused by the detector’s jump response. Based on the characteristics of the SDGSAT-1 TIS, a detection method for anomalous pixels using time-domain gradients and nonlinearity is proposed. This method also includes adaptive threshold adjustment and on-orbit data detection based on scene images. The results show that in each band, the number of flickering pixels with a detection probability exceeding 95% is fewer than 7, and the relative positions of these flickering pixels in the images are fixed. Additionally, traceability analysis of the on-orbit radiometric data reveals that the noise deviation of flickering pixels is more than 10% greater compared to normal pixels. This indicates that the flickering behavior is not random and that these pixels are more prone to flickering than others. In each band, the number of nonlinear-response pixels with a detection probability exceeding 95% is fewer than 6. Additionally, a traceability analysis of on-orbit radiometric data shows that the response deviation of nonlinear-response pixels is more than 0.7% compared to normal pixels. This result indicates that the occurrence of nonlinear-response pixels on orbit is not random and provides a new approach for early detection of anomalous pixels based on on-orbit radiometric data.

Although the thermal infrared remote sensing camera plays a pivotal role in Earth observation, and impacts the target detection, surface temperature inversion, and subsequent space missions significantly, the imaging quality of the camera is constrained by its optics, image sensors, and electronics during on-orbit operation. At the same time, the traditional blind recovery algorithms, which require extensive time for estimating intricate blur kernels, encounter challenges due to varying atmospheric conditions and other factors leading to dissimilar blur kernels across different observation scenes. In this context, this article introduces a rapid image recovery algorithm rooted in the concept of the invariant modulation transfer function (IMTF) specific to on-orbit cameras. The IMTF model remains stable and impervious to influences stemming from ground targets, atmospheric conditions, and orbital or environmental fluctuations, contingent upon the camera’s inherent characteristics. The extraction of the IMTF involves subjecting the transfer function’s region to a modified edge methodology, followed by image recovery through a hyper-Laplacian prior inverse convolution approach. The resolution of the inverse problem is achieved by employing an alternating minimization scheme. This method addresses the mitigation of imaging artifacts originating from the camera’s limitations. Comparative analysis against the state-of-the-art image recovery techniques establishes the competitiveness of the method proposed in this article, both in terms of recovery efficacy and operational efficiency. Substantiating this, experimental validation using in-orbit thermal infrared remote sensing images reveals a notable improvement in the average gradient (AG) (by a factor of 3.2), edge intensity (EI) (by a factor of 2.5), and modulation transfer function (by a factor of 1.3) of the restored images. Consequently, this approach introduces a novel perspective for enhancing the restoration of in-orbit remote sensing images.

The lunar surface temperature (LST) is one of the important thermophysical parameters of the Moon, which helps to study the radiative properties of the lunar surface. Thermal infrared emission spectra are sensitive to the thermophysical properties of the lunar surface material, and the emissivity data can be used for lunar surface composition inversion. In the past, humans have conducted hundreds of lunar exploration missions, but only a small number have been conducted in the infrared band, and typical examples include the Apollo program and the Diviner lunar radiometer experiment of the LRO satellite. Only part of the lunar surface has been explored by these missions. Sustainable Development Goals Satellite-1 (SDGSAT-1) carries out a complete observation of the lunar surface in three infrared wavelength bands (B1: 8–$10.5~\mu \text{m}$, B2: 10.3–$11.3~\mu \text{m}$, and B3: 11.5–$12.5~\mu \text{m}$) by its thermal imager spectrometer as part of its mission. In this study, the temperature-emissivity separation (TES) algorithm is used to retrieve the LST and calculate the thermal infrared band emissivity using the radiometric data obtained by SDGSAT-1 thermal imager spectrometer, and the temperature distribution of full-disk Moon and the emissivity distribution of three bands are also mapped. The temperature retrieval error is verified less than 1 K.

Accurate ground control points’ (GCPs) extraction is extremely essential to support the on-orbit geometric calibration and registrations of multitemporal and multispectral remote sensing images (RSIs). However, compared with other images, the thermal infrared (TIR) images responding to the targets’ temperature usually present low spatial resolution, poor contrast, and different mapping intensity, which make it difficult to get enough precision matches in the corresponding image pairs for GCPs’ extraction. Furthermore, with more attention to the gradient properties surrounding the interest points, the conventional feature-based algorithms generally neglect the plenty of geometric textural features of RSIs. Here, in this letter, we propose an accurate geometric-texture-based GCPs’ extraction approach for TIR RSIs. The novel textural log-polar pattern and the double constrained matching rules comprising the matching bits and differences are combined to guarantee the ultimate GCPs’ accuracy. The experimental results evaluated on TIR RSIs of Landsat 8 and GLS2000 show that the absolute matching errors of the proposed method in sample and line directions can be 0.50 and 0.47 pixels, which improve a lot in terms of three state-of-the-art methods.

Radiometric calibration of satellites is one of the core technologies for analyzing satellite data quantitatively. For large-aperture remote sensing cameras, the blackbody is placed in the rear optical path due to its weight and size. As a result, the front optics’ self-emission cannot be evaluated when the inner blackbody is observed. To achieve full optical path calibrations, stars are used as radiation calibration sources for large-aperture cameras. In high energy concentration detection systems, the point spread function (PSF), intrapixel sensitivity (IPS), capacitive coupling and sampling phase may cause some energy of the point source to be lost, resulting in an energy difference between the extended and point sources. It is important to note that conventional aperture photometry is not always the ideal method for obtaining high-precision photometry. This paper proposes a method for compensation of point-source signals based on capacitive coupling correction, PSF reconstruction and IPS model, and establishes a response conversion model for stellar and inner blackbodies. According to calibration coefficients based on stars and inner blackbody, the error between the star calibration coefficient and the inner blackbody calibration coefficient is 0.18%, which is better than 59.4% before the proposed method was applied. The error between the two methods can be stabilized within 0.3% within 140 days of the launch of the satellite.

Geometric positioning of a remote sensing image is one of the core technologies for the quantitative application of the geostationary satellite data. Affected by the change of the incident angle of the sunlight, the spatial thermal environment surrounding the remote sensing cameras (RSCs), especially the geostationary RSCs, fluctuates greatly and has a noticeable impact on the installation matrix based on the reference to the satellite body. Therefore, the spatial thermal environment will ultimately influence the camera's geometric positioning model and the final positioning accuracy. This paper proposes a novel correction method based on stellar observations for correcting geometric positioning error caused by spatial thermal deformation (STD) of geostationary optical payloads. The proposed method overcomes the drawbacks associated with current stabilization methods that involve shutting down the camera to reduce STD effects. Experimental results show that the positioning error corrected by the proposed method can be within ±1.9 pixels (2σ) at a 95% confidence level and better than the ±18 pixels before correction.