Land surface temperature (LST) is an important surface physical parameter that has received wide attention in many fields, and thermal infrared (TIR) remote sensing technology can efficiently obtain large-scale LST information based on the LST retrieval algorithms. Various LST remote sensing products are available, among which Landsat data has formed an important data accumulation with its long time series and high spatial resolution. Since it is formed by a single-channel algorithm relying on the ASTER Global Emissivity Dataset (GED), which is not spatially seamless, the LST product also suffers from missing data in the corresponding emissivity aperture region. In this study, a multiple feature reconstruction (MFR) method, which establishes the regression relationship between three types of feature parameters, namely, surface elevation, reflectance spectral, solar radiation, and LST, is developed to fill the gaps due to missing emissivity to improve the quality and availability of the Landsat LST product using the LightGBM ensemble learning algorithm. The proposed method is applied to the missing regions of the LST product in two study areas, and the spatial distribution of the existing LST products is utilized to match the filling results to obtain the corrected gap-filling LST product based on the preliminary results. Besides, the split-window algorithm retrieves the LST using the thermal radiance observed at the top of the atmosphere (TOA), and the gap-filling results of both study areas are cross-validated. The results of the accuracy validation indicate that the MFR method can fill the vacant positions in the LST product well and maintain a smooth spatial distribution. The development of an end-to-end LST retrieval algorithm directly from TOA thermal infrared observations to LST can directly avoid the occurrence of incomplete LST remote sensing products caused by insufficient auxiliary parameters. In addition, the MFR method can be also applied to under-cloud LST estimation and LST downscaling utilizing its ability to characterize LST based on other remote sensing data sources.

Accurate crop yield estimation in advance is of paramount importance for making appropriate decisions in adjusting food price and formulating food policy. Most traditional statistical yield estimation models directly capture nonlinear relationship between the original time series remotely sensed variables and yields, ignoring the complexity and non-stationarity in the remotely sensed variables. This study integrated variational mode decomposition (VMD), correlation analysis and neural network architecture to construct a Decomposition-Screening-Reconstruction-Estimation paradigm for wheat yield estimation. Firstly, VMD optimized by whale optimization algorithm (WOA) was applied to decompose the time series remotely sensed variables into intrinsic mode function (IMF) components. Secondly, effective IMF components with strong correlation to the original time series remotely sensed variables were identified and reconstructed as the key time series features. Finally, the performances under the proposed paradigm of Transformer model and baseline models including support vector regression (SVR), random forest (RF) and long-short term memory (LSTM) were compared. The results showed that WOA-VMD-Transformer (WVTF) (R2= 0.66, RMSE = 459.09 kg/ha, MRE = 8.22 %) outperformed other models, and deep learning models (LSTM and Transformer) based on the new paradigm exhibited superior performance compared with traditional models (SVR and RF). In addition, WVTF (R2=0.54, RMSE=468.19 kg/ha, MRE=6.13 %) provided better performance at the sampling sites compared with previous proposed model (R2= 0.45, RMSE = 738.63 kg/ha, MRE = 8.21 %). The estimated wheat yields of 2019-2024 based on the optimal model were identical to the distribution of actual yields. In conclusion, the framework for yield estimation under novel paradigm extracts the original remotely sensed variables into key time series features, which provides an important reference for regional crop yield estimation and agricultural development.

Land surface temperature (LST) is a critical physical parameter affecting energy and water exchange that has attracted much attention in various fields, such as environmental protection, agriculture, and climate change. Studies on spatially continuous and high-resolution LST retrieval methods, which can be efficiently acquired using thermal infrared (TIR) remote sensing technology, have been developed for many years, resulting in various LST remote sensing products. The typical mechanism thermal radiative transfer model is based on the assumption that the land surface is flat, with the TIR remote sensing image of the spatial resolution of the enhancement of the ability to observe the land surface of the three-dimensional geometric structure of the fine observation, due to the terrain caused by the topographic effect caused by the topography of the undulation becomes non-negligible, the assumption of flat surface may cause apparent errors. Some LST retrieval algorithms considering topographic effects have also been proposed recently. However, they are still inaccessible due to dependence on emissivity or atmospheric parameters, which limits the accuracy and timeliness of the retrieval algorithms. In addition, various machine learning algorithms for end-to-end LST retrieval have been proposed, which utilize their ability to handle complex nonlinear relationships to retrieve LST without external parameters. However such models currently do not fully consider the topographic effect due to a lack of account of the radiative transfer process in undulating terrain conditions. In this study, utilizing the ability of convolutional neural networks to extract spatial features from adjacent pixels, a radiative transfer model-driven convolutional neural network (CNN) model is proposed to realize the end-to-end retrieval of LST, considering the topographic effect. During training, a computational method based on ambient radiance scattered from the surrounding adjacent pixels in the improved radiative transfer model is used to obtain a local-scale simulation dataset covering different LSTs, emissivity, terrain undulations, and atmospheric conditions. The proposed CNN model is trained on this basis, and the theoretical accuracy is evaluated using the simulation dataset. The model has been applied to long-time-series Landsat-9 TIR remote sensing images. The accuracy is verified using terrain-corrected (TC) LST products. The results show that the new method proposed in this paper can effectively eliminate the topographic effect in TIR remote sensing observations and obtain accurate LST retrieval results, requiring only brightness temperature and digital surface model data.

Urban land surface temperature (ULST) is one of the core parameters in monitoring the urban thermal environment, which has received extensive attention in several study and application areas. Thermal infrared (TIR) remote sensing technology can efficiently observe large-scale land surface thermal radiance information and is a critical approach used to obtain ULST quickly. Traditional LST retrieval algorithms are conducted using the classical radiance transfer equation (RTE) based on the assumption that the land surface is flat, which may be challenging to hold for complex urban landscapes. Moreover, with the improvement of the spatial resolution of remote sensing images, the influence caused by the geometric structure will be more obvious. Various urban thermal radiance transfer models have been proposed and successfully applied to TIR remote sensing images with tens of meters spatial resolutions, such as Landsat, ECOSTRESS, and Gaofen-5. Current airborne TIR sensors can observe remote sensing images with ultra-high spatial resolution (sub-meter). In this paper, using the ensemble learning method based on the ultra-high spatial resolution urban thermal radiance transfer model (UHURT), a new retrieval algorithm is developed to estimate the LST directly from the observed brightness temperature. The proposed new algorithm applies to ultra-high spatial resolution remote sensing images. It has the end-to-end advantage of not relying on atmospheric parameters or land surface emissivity, known as in traditional algorithms, thus avoiding the limitations due to the lack of available input data. Validation results based on the simulation dataset showed that the proposed algorithm has higher theoretical accuracy than the traditional split-window algorithm. As the sky view factor (SVF) decreases, the accuracy advantage becomes more pronounced, growing from 0.149 K (SVF = 1.0) to 1.085 K (SVF = 0.25). The application results in the remote sensing image also indicated that the results of the proposed algorithm (RMSE = 2.093 K) are more accurate than those of the SW algorithm (RMSE = 2.490 K), and the correlation between the resultant error and building density is lower, which can accurately reduce the geometric effect to obtain the ULST better.

Identifying land cover changes based on hyperspectral images (HSIs) has been a research hotspot in the field of remote sensing. In recent years, deep learning-based change detection (CD) methods have advanced the development of this subject due to their powerful feature representation capabilities. However, it is difficult for these methods to mine changed information between bi-temporal HSIs with limited labels. To overcome this limitation, we propose a graph-based hyperspectral CD framework using difference augmentation and progressive reconstruction (ARCD), which enhance the recognition ability of changes of HSIs with limited labels. This framework consists of three components: 1) a dual-brach multi-scale dynamic GCN (DMGCN) sub-network, which is developed to emphasize the changed information and learn global features of HSIs at various scales; 2) a difference augmentation feature fusion (DAFF) module, which is designed to fuse spectral-spatial augmentation information and the difference information to accurately capture discriminative features for the changes between bi-temporal HSIs; 3) a progressive contextual information attention reconstruction (PCAR) module, which is proposed to focus on key information in the context, and progressively reconstruct multi-level features to reduce semantic gaps between different scale features. ARCD not only enhances the representation ability of changed features, but also alleviates the demand for HSI labels. We test the performance of ARCD on four hyperspectral datasets. Quantitative and qualitative results reveal that it outperforms some state-of-the-art methods with limited labels.

Thermal infrared (TIR) remote sensing is an important technological tool for observing large-scale land surface thermal radiance and can obtain the spatially continuous land surface temperature (LST), a critical land surface physical parameter of great interest in several fields. After decades of development, various LST retrieval algorithms have been proposed. However, current studies indicated that the commonly used retrieval algorithms show a decrease in the accuracy of the results under humid atmospheric conditions, and the theoretical analysis of the phenomenon needs to be developed. This study derives the LST error as a function of atmospheric parameters (transmittance, upward radiance, and downward radiance) directly based on the TIR radiative transfer equation. Compared with the TIR channel, the mid-infrared (MIR) channel has less water vapor absorption, is more insensitive to water vapor, and has a larger transmittance, which is expected to improve the accuracy of LST retrieval under humid atmospheric conditions. In this study, a typical simulation dataset under various atmospheric and land surface conditions is constructed based on the MIR channels of MODIS remote sensing data. Analysis of the retrieval results based on the simulation datasets shows that the MIR channels have more minor errors for the same level of atmospheric errors. With the growth of column water vapor (CWV), the error of the split-window (SW) algorithm constructed based on the TIR channel increases. In contrast, the accuracy of the algorithm developed by MIR channels is more stable, and the advantage of the accuracy in humid atmospheric conditions is more prominent. Two SW algorithms are applied to nighttime TIR and MIR remote sensing images observed by Aqua MODIS, and the validation results obtained based on SURFRAD ground sites also showed that the two MIR SW algorithms achieved the accuracy advantage of 0.425 K (SW1_TIR: 2.582 K / SW1_MIR: 2.157 K) and 0.525 K (SW2_TIR: 2.624 K / SW2_MIR: 2.099 K), which indicated that the MIR-SW algorithms can more accurately retrieve the LST under humid atmospheric conditions.

Accurate and timely crop mapping using remote sensing technology is crucial for precision agriculture, yield estimation, and food security. Deep learning models trained with proper loss functions are widely used in crop mapping and have achieved promising results. However, most of the existing loss functions focus on loss optimization in a universal way, i.e., problems regarding sample imbalance, and neglect the uniqueness of crop mapping task, for which its target often shows phenological characteristics. Given this, this letter proposes a crop phenological prior cross entropy loss (PPCE) function, which focuses on guiding the training processing in the direction where crops can be better identified. It is practical and easy to use. The phenological prior is quantified using normalized difference yellow index and normalized difference vegetation index obtained in different growing periods. Under the supervision of PPCE, if a crop pixel is misclassified to other class, the prior knowledge will increase the contribution of its loss to the final loss and thus guide the network to extract more discriminative features for crop mapping. To demonstrate the performance of PPCE, five widely used loss functions combined with three typical deep learning models (LSTM, DNN, and 1D-CNN) are compared. Experimental results show better performance of PPCE than the existing loss functions with different deep learning models.

The land surface temperature (LST) of urban is a key factor in the field of urban environmental monitoring, and thermal infrared (TIR) remote sensing is an efficient method to obtain it. An important assumption of the traditional thermal radiance transfer model is that the land surface is flat, which has now proven difficult to hold under the urban landscape. Most of the existing urban thermal radiance transfer models have been developed for remote sensing images with a spatial resolution of tens of meters. Currently, airborne TIR sensors have the observation capability to acquire remote sensing images with an ultra-high spatial resolution (1 cm to 1 m), and the model needs to be improved. This paper proposed a new ultra-high spatial resolution urban thermal radiance transfer model (UHURT) after analyzing the transfer processes within the urban canopy at ultra-high spatial resolution. Various radiance components, the emitted radiance, reflected atmospheric downward radiance, and adjacent radiance, were modeled separately. The results of the traditional model and the UHURT model were compared with the results of a ray-tracing computer simulation model, which showed that the new model successfully quantifies the multiple scattering and adjacent effects, and obtained images closer to the computer simulation images. Besides, the LST retrieval of the computer-simulated images was performed using the traditional model and the UHURT model, and the proposed model successfully reduced the errors of the retrieval results and weakened the spatial correlation between the residual distribution and the geometric characteristics of the urban landscape.

The Urban Canopy Multiple-scattering thermal Radiative Transfer (UCM-RT) model, incorporating the effects of urban geometry and adjacent thermal radiation from neighboring pixels, depicts the process of thermal radiation transfer on the urban surface, and therefore provided new opportunity to develop new retrieval algorithms for urban land surface temperature (ULST). This paper aims at developing an urban split-window (USW) algorithm for deriving ULST from high-spatial-resolution thermal infrared (TIR) data from the Visible and Infrared Multispectral Sensor (VIMS) onboard Chinese GaoFen-5 (GF-5) satellite. The VIMS provides 4-channel TIR image with a spatial resolution of 40 m. The coefficients of the USW algorithm were obtained based on several subranges of atmospheric column water vapors (CWV), emissivity and sky view factors (SVFs) under various land surface conditions, by removing the geometry, adjacent and atmospheric effects. Methods of estimating urban pixel emissivity and CWV in urban areas were also conducted. The sensitive analysis of instrument noise and uncertainty of CWV, pixel emissivity and SVFs demonstrated the reliability of the USW algorithm in ULST retrieval. The accuracy evaluation shows that the root-mean-square errors of the ULST results is less than 0.7 K in theory. Compared with the conventional SW algorithms and publicly released LST products, the USW algorithm obtained better results in estimating ULST, especially in high-density building areas. Finally, the USW algorithm is expected to be beneficial to the application of multiple thermal infrared sensor data, for example, the newly launched GF-5 No.2 satellite images.

As a critical parameter of the land surface energy balance, land surface temperature (LST) has received extensive attention in various fields. Thermal infrared (TIR) remote sensing can efficiently obtain large-scale, long-time-series information on land surface thermal radiance. Multiple algorithms, including physics-based and deep learning algorithms, have been proposed for obtaining LST from the observations. In algorithm analysis, the theoretical performance is typically evaluated through simulated data representing atmospheric and land surface environmental conditions that vary globally before applying it to authentic TIR remote sensing images and validating the accuracy using ground-measured data. However, due to the complexity of the observation environment, errors in results obtained from validation using ground-measured data tend to be larger than theoretical errors obtained using simulated data, which limits the performance of the algorithm in practical applications. Obtaining globally covered, representative ground-measured data and synchronized observations of remote sensing images are costly, making it difficult to provide enough data to develop new algorithms. The transfer learning method can learn from the pre-trained deep learning model that applies to the source task, and fine-tuning using only a small number of samples from the target task can result in a well-performing model. This paper proposes a modified transfer-learning-based (TL) land surface temperature retrieval algorithm to pre-train a knowledge-driven deep neural network model using a simulation dataset and then use a small amount of ground-measured data for tuning to obtain the final LST retrieval model. The new algorithm was applied to the remote sensing data observed by the Landsat-8 Thermal Infrared Sensor (TIRS), and the validation results based on the ground-measured data and global Landsat-8 LST products showed that the RMSE of the fine-tuned model result was about 0.4 K lower than the Landsat-8 product, and about 0.3 K lower than the pre-trained model results, reaching 2.2 K. Moreover, the results are in good agreement with the Landsat LST product in multiple regions worldwide with different land cover types, which demonstrated the effectiveness and stability of the proposed TL algorithm.

Land surface temperature (LST) is a pivotal parameter in many study areas. At present, numerous algorithms are available to retrieve accurate LST from different satellite thermal infrared (TIR) observations. However, rare studies focus on simultaneous LST and land surface emissivity (LSE) retrieval from the TIR measurements of MERSI-II onboard Chinese FengYun-3D satellite designed with a spatiotemporal resolution of 250 meters and five days, that just bridges the Terra/Aqua-MODIS and Landsat-TIRS observations. Although simultaneous LST and LSE retrieval could be achieved by the existing temperature-emissivity separation (TES) method, only two of the three MERSI-II TIR channels are with the spatial resolution of 250 meters leading to the difficulty of directly applying the traditional TES method. Inspired by the TES method and the theory of temperature-independent spectral indices (TISI), this study proposed a new scheme for deriving the 250-meters LST and LSE simultaneously from the MERSI-II TIR data. T-based validations using the ground measurements indicated that the LST retrieval accuracy was about 3.19 K and 2.51 K in the daytime and nighttime, respectively. Cross-validations taking MODIS LST product as the references showed that errors in the retrieved LST was <2.1 K in the daytime while <1.4 K during the nighttime. Overall, results showed that the proposed method can be used to retrieve global LST and LSE from the MERSI-II data, which can facilitate their applications in relevant fields.

Thermal infrared (TIR) remote sensing observation signal is influenced by both atmospheric and land surface conditions that are difficult to separate with conventional multichannel TIR data. Because of the advantage of channel wealth, hyperspectral TIR data can simultaneously estimate the land surface and atmospheric parameters using neural network models or integrating them with physical models. However, the commonly used neural network models do not fully explore the correlation between different channels by treating the input data as discrete features. Thus, this study aims to develop a new deep neural network (DNN) by combining the long short-term memory (LSTM) network and convolutional neural network (CNN) for estimating land surface temperature (LST), emissivity, atmospheric transmittance, upward radiance, and downward radiance more accurately. By applying on the thermal airborne hyperspectral imager (TASI) simulation dataset covering global atmospheric conditions with 32 channels in$8.0- 11.5\,\,\mu \text{m}$, the proposed model achieved results with the LST error of 0.95 K, the emissivity error of less than 0.012 for each channel, and the accuracy of three atmospheric parameters has also been improved compared with the current neural network models. Our model has been applied to a real TASI image, and its validity was further proved by the ground measurement validation data. Therefore, it can provide more reliable initial values for physical optimization models.

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature at the top of the atmosphere in two adjacent TIR channels and thus is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides 2-channel TIR images with the same 100m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this paper, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 K to 2.87 K in various regions.

Thermal infrared remote sensing can acquire large-scale land surface thermal radiance effectively. However, the observed data are affected by surface and atmospheric conditions. Traditional methods require some prior knowledge, such as emissivity in split-window algorithm and atmospheric correction in temperature–emissivity separation algorithm. This information is difficult to obtain directly and accurately. Hyperspectral thermal infrared data provide the possibility for simultaneous retrieval of atmospheric parameters, land surface temperature (LST), and emissivity because of their abundant band information. This study proposed a feature-band linear-format hybrid (FebLihy) algorithm by combining a deep neural network (DNN) model and a physical model with thermal airborne hyperspectral imager (TASI) data. The proposed algorithm was divided into three steps. First, the radiative transfer equation was converted into a linear form, and seven feature bands were chosen to reduce the unknowns. Second, the initial values of atmospheric and land surface parameters were estimated with the DNN model. Finally, least-squares optimization was used in the physical model to retrieve the final results. Results of the simulation data showed that the root-mean-square error (RMSE) of LST was 0.86 K, the RMSE of emissivity was less than 0.015, and the accuracy of atmospheric parameters was improved effectively by the physical model. The FebLihy algorithm was applied in a real TASI image in Fuyun County and verified with CE312 ground measurement data. Accurate results were achieved. The FebLihy algorithm will be optimized in terms of model and data in the future study.

Land surface temperature (LST) is a crucial parameter in the energy and material balance of land surface system. The angle effect of LST makes the accuracy of LST restricted and limits the application of remote sensing LST product. In order to eliminate the influence of viewing angle, this study proposed a novel method to perform angular normalization by constructing a feature space of surface emission radiance and fractional vegetation coverage (Radiance-FVC space). The proposed approach is applied in Hetao Plain as an example. It is found that the Root Mean Square Error (RMSE) can reach 5.1K, and the angular normalization effect is more significant for pixels with larger viewing zenith angle.

Building extraction from high-resolution remote sensing images has widely been studied for its great significance in obtaining geographic information. Many methods based on deep learning have been tried for the task; however, there is still much to explore about designing layers or modules for remote sensing data and taking full use of the unique features of buildings like shape and boundary. In this letter, an end-to-end network architecture based on U-Net is proposed. The U-Net architecture is modified with Xception module for remote sensing images to extract effective features. Also, multitask learning is adopted to incorporate the structure information of buildings. Two standard data sets (Massachusetts building data set and Vaihingen Data set) of high-resolution remote sensing images are selected to test our model and it achieves state-of-the-art results.

Land surface temperature (LST) is a key surface feature parameter. Temperature and emissivity separation (TES) and split-window (SW) algorithms are two typical LST estimation algorithms that have been applied to a variety of sensors to generate LST products. The TES algorithm can synchronously obtain LST and emissivity, but it requires high accuracy for atmospheric correction of the thermal infrared (TIR) data and does not perform well for surfaces with low spectral emissivity contrast. On the contrary, the SW algorithm can retrieve LST without detailed atmospheric data because the linear or nonlinear combination of brightness temperatures in the two adjacent TIR channels can reduce the atmospheric effect; however, this algorithm requires prior accurate pixel emissivity. Combining the two algorithms can improve the accuracy of LST estimation because the emissivity calculated from the TES algorithm can be used in the SW algorithm, and the LST from the SW algorithm can then be applied to the TES algorithm as an initial value to refine emissivity and LST. This paper investigates the aforementioned hybrid algorithm using Chinese Gaofen-5 satellite data, which will provide four-channel data for TIR at 40 m for synchronously retrieving LST and emissivity. The results showed that the hybrid algorithm was less sensitive to instrument noise and atmospheric data error, and can obtain LST and emissivity with an error less than 1 K and 0.015, respectively, which is better than those obtained with the single TES or SW algorithm. Finally, the hybrid algorithm was tested in simulated image and ground-measured data, and obtained accurate results.

In hyperspectral thermal infrared remote sensing, iterative spectrally smooth temperature / emissivity separation (ISSTES) is currently the most popular method to retrieve land surface temperature (LST) and emissivities (LSEs) at the same time. However, a serious problem may occur when noise reaches certain intensities, which causes ISSTES to fall into a false minimum, and thus the errors of LST and LSEs are far beyond tolerance. In this paper, both simulated and measured data were used to show how the problem would occur, and the ISSTES-Extreme (ISSTES-E) method was proposed to fix the problem. The results reveal that the new method is able to prevent the false minimum when the original method fails to come to a valid answer.

Damaged building detection from high spatial resolution remote sensing image helps to rapid disaster losses assessment. However, the majority of traditional methods relies on only a single category feature of the damaged building. This letter presents a new strategy for detecting damaged buildings from postquake remote sensing image by multiple-feature analysis, in which the integrity of the building edge and the interior roof was both considered. The intactness of the building edge was assessed by proposing a new feature parameter, edge significance (ES), ES using significance test to quantify the difference between the gradient values on the edge and in the edge buffer. In addition, the gradient orientation inside the building was analyzed and local gradient orientation entropy (LOE) parameter was adopted to determine whether the interior roof was damaged. In general, damaged buildings have lower ES values because of broken edges and higher LOE values owing to debris, final decision was made on the basis of both feature parameters. A Quickbird image of Yushu, China, was used in the experiment and, among a total of 327 buildings, 266 were detected correctly. The overall accuracy was 84.10%, which is better than traditional methods.

The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data.

This paper presents a novel deep hierarchical representation and segmentation approach for high resolution remote sensing image understanding. An information extraction approach using deep hierarchical exploitation for remote sensing image is presented. The key idea is that we adopt a fast scanning image segmentation within a deep hierarchical feature representation framework, using a deep learning technique to split and merge over-segmented regions until they form meaningful objects. The contribution is to develop an effective procedure for multi-scale image representation to address the issue of information uncertainty in practical applications. We test our method on two optical high resolution remote sensing image datasets and produce promising experimental results in the form of multiple layer outputs, which confirm the effectiveness and robustness of the proposed procedure.

Road damage detection from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pair of pre-disaster and post-disaster road data for change detection are difficult to obtain due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e. remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, aspect ratio are selected form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads were detected by applying the knowledge model. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake in May 15, 2008. The results show that the producer's accuracy (PA) and user's accuracy (UA) reached about 90% and 85% respectively, indicating that the proposed method is effective for road damage detection. This approach also significantly reduces the need for pre-disaster remote sensing data.

This paper presents a new method to detect damaged buildings caused by earthquake from high spatial resolution remote sensing image. We found that the probability of multiple gradient orientations is greater in a local area within a damaged building than that in a local area within an intact building. Therefore, a new feature (Local Gradient Orientation Entropy, LGOE) was put forward to determine whether a building was damaged. First, gradient information was obtained by Prewitt gradient operator. Second, the gradient orientation entropy of one pixel was calculated in a local 3 ×3 window. Last, average LGOE value within a building boundary was counted. In general, damaged buildings have higher LGOE values because of their irregular texture. Therefore, an optimum LGOE threshold value (LGOET) was set to detect damaged buildings. The experiment results of Yushu earthquake using a Quickbird image demonstrated that our method was effective. Of the total 101 buildings, 87 were detected correctly, the overall accuracy was 86.14%, and the overall kappa coefficient is 72.25%.

Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.

This letter presents a new approach for rapid automatic building extraction from very high resolution (VHR) optical satellite imagery. The proposed method conducts building extraction based on distinctive image primitives such as lines and line intersections. The optimized framework consists of three stages: First, a developed edge-preserving bilateral filter is adopted to reduce noise and enhance building edge contrast for preprocessing. Second, a state-of-the-art line segment detector called EDLines is introduced for the real-time accurate extraction of building line segments. Finally, we present a graph search-based perceptual grouping approach to hierarchically group previously detected line segments into candidate rectangular buildings. The recursive process was improved through the efficient examination of geometrical information with line linking and closed contour search, in order to obtain more reasonable omission and commission rate in building contour grouping. Extensive experiments performed on VHR optical QuickBird imageries justify the effectiveness and robustness of the proposed linear-time procedure with an overall accuracy of 80.9% and completeness of 87.3%. This method does not require user intervention and thereby has the potential to be adopted in online applications and industrial use in the near future.

This paper presents a novel hierarchical feature representation for geospatial objects detection from optical very high resolution (VHR) satellite imagery. An information extraction approach using multi-scale and hierarchical exploitation for remote sensing image is presented. The key idea is that we adopt a morphological pyramid-based framework for geospatial objects detection in VHR imagery, with a combination of morphological pyramid exploitation and a novel hierarchical feature representation metric, in order to develop an efficient procedure for multi-scale analysis of geospatial object detection to address the issue of information uncertainty in practical applications. We test our method on optical VHR QuickBird satellite imagery and obtain promising experimental results, which confirm the effectiveness and robustness of the proposed procedure.

Road is a kind of very typical artificial object. Road extraction from multi-scale remote sensing images is significant both in military field and in people's daily lives. With the development of remote sensing technology, the scale of remote sensing images that can be obtained becomes various. Therefore, the research of multi-scale remote sensing images is getting more and more attention and it is really a challenging task in the field of image processing. In this paper, a method of road extraction from multi-scale remote sensing images is proposed. Firstly, the textures are extracted and added to the bands of the original image. The filtering, resampling and segmentation operations are then implemented. Next, the spectral characteristics and textures of roads on the remote sensing images are statistically analyzed, and the changes of those on multi-scale remote sensing images are obtained. Then, considering the road characteristics displayed on remote sensing images, some parameters of spectral characteristics and textures are selected to extract roads using the object-oriented method. Finally, the results of road extraction are post-processed based on the opening and closing operation of mathematical morphology. This study has great significance in areas such as features optimization, target recognition, building feature database and improving the utilization of remote sensing data.

The paper presents an approach for building damage detection from high resolution remote sensing image using multi-feature analysis and the fuzzy reasoning procedure. The selected area of our study is in Yushu, which was strongly hit by 7.1-magnitude earthquake. The study area contains 101 buildings, of which 46 are collapsed and 55 are un-collapsed. First, the buildings were selected one-by-one from the GIS data and remote sensing image. Second, three categories of features were analyzed to describe the differences between the collapsed buildings and un-collapsed ones, including spectral feature, texture feature and gradient feature. Last, a final decision was made through considering the variety of feature parameters utilizing fuzzy reasoning. The overall accuracy of building damage detection was 91.09%, of the total 46 collapsed buildings, 42 were detected correctly by the proposed approach, giving 91.30% producer's accuracy.

This paper presents an automatic procedure for rapid building extraction from optical very high resolution (VHR) satellite imagery. Classical extraction models are always complex and time-consuming. The optimized process of building extraction consists of three main rapid stages: edge-preserving and smoothing bilateral filter, line segment detection, perceptual grouping polygonal building boundary. Firstly, we use bilateral filter to smooth original image with edge-preserving. Secondly, a state-of-the-art line segment detector (LSD) algorithm gives highly accurate building contour segments. Finally, we apply the perceptual grouping approach based on graph search to organize detected contour line segments of interested buildings. We test our method on optical VHR QuickBird satellite imagery and obtain promising experimental results with overall accuracy of 79.1%, which confirm the effectiveness and robustness of this linear-time procedure.