Anomaly detection is one of the most popular research topics in hyperspectral remote sensing. A variety of traditional model-driven methods fail to reveal features of data with diversity due to monotonous, fixed analytical modes. This paper analyzes mathematical-statistical properties of hyperspectral images (HSIs) and proposes an interesting approach of information entropy estimation based on point-set topology (IEEPST) to resolve anomaly detection from a brand new perspective, thus eliminating the limitations caused by the data-model discrepancy. Specifically, the original HSI data are mapped into topological spaces to enable ordered arrangements, in preparation for revealing data features. Particularly, information entropy estimation is introduced for the first time in the adoption of point-set topology to adequately unravel the data arrangements in topological spaces, whereby the land cover information is efficiently extracted for detection. Experimental results demonstrate that IEEPST accommodates both detection accuracy and computational efficiency, and is highly competitive with other sophisticated and state-of-the-art methods.

The current classification systems are established from the perspective of terrain information that humans can collect on the surface, in order to meet different research purposes, research areas, research objects, or application and management requirements. The extraction of land use information contained in these distinctive classification systems mainly relies on remote sensing technology. However, due to the lack of strict connection between remote sensing technology and the current classification system and application, remote sensing technology has not been fully utilized in land use classification. Therefore, based on the multi-source remote sensing observation system’s representation of the ground in the spatiotemporal spectral dimensions and the principle of stable and classifiable of land cover, this study constructs a three-level remote sensing intelligent interpretation classification system: the first level -recognizable using "spectral characteristics ", the second level-identifiable by overlaying " temporal characteristics " or " temporal and spatial characteristics ", the third level-identifiable by overlaying "spectral physicochemical parameters". This classification system is a remote sensing intelligent interpretation product system driven by satellite remote sensing, which only includes remote sensing interpretable elements and does not mix management attributes. Taking the area of Beijing as an example, the first and second level was classified, demonstrating the feasibility of the technical approach of the classification system.

Due to limitations of satellite imaging systems, hyperspectral image (HSI) often suffers from incomplete coverage, with certain regions of the study area missing. Data fusion and reconstruction are effective approaches to resolve the contradiction in spatial and spectral domains, where related theories have intensively developed in recent years. However, existing fusion methods are mostly applicable to simulated data and are challenging to apply to real data. In this paper, we propose an unsupervised fusion spatial reconstruction network namely UFSRnet, which not only reconstructs the missing regions of HSI but also learns the differences between heterogeneous data adaptively. Specifically, a sensor radiation deviation correction (SRDC) module is designed to tackle the disparities between heterogeneous data adaptively. The model demonstrates commendable performance across both simulated and real data sets.

A hyperspectral pushbroom sensor scans objects line-by-line using a detector array, and a cross-track illumination error (CTIE) exists in the imagery acquired in this way. When the illumination of the individual cells of the detector is not aligned well, or if some of the cells are degraded or old, the acquired images will exhibit nonuniform illumination in the cross-track direction. As additive Gaussian noise is found widely in hyperspectral images (HSIs), we develop a unified mathematical model that describes the image formation process corrupted by the CTIE and additive Gaussian noise. The CTIE produced by line-by-line scanning is replicated and modeled as an offset term with the equivalent values in the direction of flight. The main contribution of this study is the development of a hyperspectral image cross-track illumination correction (HyCIC) method, which corrects the cross-track illumination using column (along-track) mean compensation with total variation and sparsity regularizations, and attenuates the Gaussian noise by using a form of low-rank constraint. The effectiveness of the proposed method is illustrated using semireal data and real HSIs. The performance of the proposed HyCIC is found to be better than other existing methods.

Based on the systematic analysis of the current situation and main problems of ecological restoration in China, this paper summarized the technical flow of territorial space ecological protection and restoration project, as well as the core requirements for geospatial data. Then combined with the development status of China's natural resources Landsat and their major technical parameter characteristics, the core supporting product system of satellite remote sensing was designed, and an idea of Lifecycle stage-Theme-Scene-Element was proposed. Finally, by taking some typical cases as examples, the main application modes of the products were explained. The product system and application mode proposed can not only help the government improve the efficiency of ecological restoration management and data consistency by using remote sensing data and Hi-technologies such as artificial intelligence and quantitative remote sensing, but also help to promote widely application of China's natural resources Landsat in government management.

Recently, convolutional neural networks (CNNs) have been widely applied to hyperspectral image (HSI) classification due to their detailed representation of features. Nevertheless, the current CNN-based HSI classification methods mainly follow a patch-based learning framework. These methods are nonglobal learning methods, which not only limit the use of global information but also require a high computational cost. In this letter, an image-based global learning framework is introduced to HSI classification. Based on this framework, we propose a dual-channel convolutional network (DCCN) for HSI classification to maximize the exploitation of the global and multiscale information of HSI. The experimental results conducted on two real hyperspectral datasets indicate that our method is superior to other related methods in terms of both efficiency and accuracy for HSI classification.

Water clarity (Zsd) is a widely used quality indicator that can be estimated from remote sensing imagery. China’s newest generation Advanced HyperSpectral Imager (AHSI) onboard the ZY1-02D satellite is expected to enable accurate water clarity retrieval for inland waters, since AHSI can provide abundant band choices while its 30-m spatial resolution is advantageous for monitoring small inland water bodies. In this study, to retrieve Zsd from the ZY1-02D imagery for inland waters with varying turbidities, we propose a classification-based, semi-analytical method in which the red/blue band ratio is employed to distinguish clear to moderately turbid water and highly turbid waters. Two Quasi Analytical Approaches (QAAs), QAAv5 and QAAm14, are used to estimate the total absorption coefficient (a(λ)) and the backscattering coefficient (bb(λ)) for clear to moderately turbid water and highly turbid waters, respectively. The estimated a(λ) and bb (λ) are utilized to obtain the diffuse attenuation coefficient Kd, followed by the Zsd calculations. Compared with 70 matchups of in situ measured Zsd values (0–6.5 m), the ZY1-02D image-derived Zsd achieved an R2 of 0.98, with an average unbiased relative error and root mean square error of 29.1% and 0.52 m, respectively. In addition, the proposed method can yield Zsd with higher accuracies than that of optimized empirical models. Therefore, the ZY1-02D AHSI imagery can retrieve reliable Zsd for both clear (> 3 m) and turbid waters (0–3.0 m), thereby serving as a useful satellite data source for monitoring the water clarity of large-scale inland water bodies.

Atmospheric absorption bands centered at 2.7 micron are used in missile warning systems for target detection and tracking. Since image simulation is an important tool for sensor development, relevant research should be conducted for sensors using the 2.7 micron absorption bands. In this paper, we propose a surface reflectance image simulation method for this absorption bands, to prepare surface input images for corresponding end-to-end simulation. Considering that surface reflectance is related to the surface material type, reflectance images in the absorption bands are simulated from abundance inversion and spectral mixing. Specifically, spectra in spectral libraries are used as endmembers for data source images, and abundance inversion are conducted to acquire abundance maps of these types of materials. Then, spectral mixing is conducted to generate reflectance images with reflectance in the absorption bands of endmembers and abundance maps. Accuracy analysis shows this method is feasible and with good accuracy.

Mid-infrared atmospheric absorption bands centered at 4.3 μm are applied in target detection. Image simulation, as an important tool for the adaption and optimization of a sensor, ought to be conducted for the development of instruments using this spectral range. In this paper, a top-of-atmosphere (TOA) image simulation method is proposed, and this method is tested on two bands of the sensor SPIRIT III (band S1: 4.21-4.37 μm, S2: 4.23-4.47 μm). Band translation models are established for the generation of surface emissivity images, and an analytic radiative transfer model is modified and utilized to simulate TOA radiance fast and accurately. Accuracy analysis of the proposed method shows relative errors of within ±6% and ±1% in simulated surface emissivity and TOA radiance, respectively. Moreover, image simulation is often used for band selection in the sensor design stage. To illustrate how our proposed simulation method was applied in band selection, we used simulated TOA radiance of bands S1 and S2 as an example and compared their possibility of false alarms caused by high-temperature objects. Experimental results show that high-temperature objects are more unlikely to become false alarms on band-S1 images. Therefore, the spectral range of S1 is a better option for target detection application than S2. This TOA simulation method can be also applied in band selection among other 4.3-μm absorption bandwidths, as was done in this paper.

Land surface temperature (LST) is required by a series of surface studies and usually estimated using thermal infrared remote sensing data. In this paper, we propose a method to retrieve LST for MODIS data using its mid-infrared bands 22 and 23. As the central wavelengths of bands 22 and 23 are quite close, we assume that (1) band-averaged surface e-missivities are equal in these two bands, and (2) the Planck integration between these two bands comply to a statistical relationship. Based on these two assumptions, land surface temperature is retrieved after atmospheric correction on the radiance images. A test case is selected to estimate LST using our proposed method. The estimated LST image shows good consistency with the corresponding standard MODIS land surface temperature product, with the relative errors of ±1%. The experiment results suggest this methodology is effective to retrieve MODIS land surface temperature quickly.