In remote sensing semantic segmentation, imperfect labels are prevalent due to the complexities of data acquisition and annotation processes. Although recent approaches to noisy label correction in remote sensing segmentation have shown promising results, challenges remain in accuracy and generalizability, given the incomplete consideration of the complex mixing involved. Nearly all methods should address three key challenges to identify and correct noisy labels: when to select labels, which labels to select, and how to handle the selected labels. We propose a novel label correction framework, Adaptive Consistency-Guided Object-Level Label Correction with Mean Teacher (ACOC-MT), which effectively addresses all three of these challenges. The ACOC-MT framework determines when to conduct label correction through the Stable Early Learning Detection module, selects noisy labels using the Consistency Threshold Mask module, and corrects the labels through the Object Label Correction module. To validate against real-world noisy labels rather than simulated ones, we constructed three real-world noisy-labeled datasets, CPCTC-N, BBD250-N, and CFD-N, from three different remote sensing scenarios. Extensive experiments on these three datasets demonstrate the efficacy and superior performance of our approach.

Remote sensing visual grounding (RSVG) aims to accurately localize specific targets in remote sensing (RS) images based on natural language descriptions. However, existing RSVG datasets often contain overly simplistic textual descriptions, exhibit imbalanced object size distributions, and lack semantic connections between targets and the surrounding contexts. Moreover, current approaches rely on global visual features for region-level localization, while the interaction between visual and textual modalities remains limited. To address these challenges, we propose improvements from both the dataset and algorithm perspectives. First, we explore the semantic correlation between remote sensing objects and their scene context. Based on high-resolution Gaofen satellite imagery, we expand several typical object categories and construct richer textual descriptions that reflect object-background associations. By refining and extending the existing DIOR-RSVG dataset, we build a new dataset named DGF-RSVG. Second, to enhance the semantic alignment between global visual features and textual features, we propose a novel global vision-language multimodal feature interaction enhancement module (GME). In parallel, we design a local attention enhancement module (LAE) to facilitate fine-grained interaction between object-related textual features and regional visual proposals. These two modules form the foundation of our newly developed detection framework: the global-local attention enhanced detector (GLAED). Extensive experiments show that GLAED achieves state-of-the-art performance on the DGF-RSVG dataset, outperforming the closest competitor by 4.3% in [email protected]. It also achieves highly competitive results on the DIOR-RSVG dataset, demonstrating the effectiveness of both our proposed dataset and model framework.

Change detection (CD) based on high-resolution remote sensing images can be used to monitor land cover changes, which is an important and challenging topic in the remote sensing field. In recent years, with the development of deep learning, CD methods based on deep learning have achieved good results in the field of CD. However, most current CD methods use single- or dual-stream networks to extract change features, which is insufficient to extract and learn bitemporal change information thoroughly. This article proposes a triple-stream network (TSNet) with cross-stage feature fusion for CD in high-resolution bitemporal remote sensing images. First, to obtain highly representative deep features in the original image, we perform feature extraction on bitemporal remote sensing images and their concatenated image with a dual-stream encoder and a single-stream encoder, respectively. Then, the bitemporal multiscale features extracted by the dual-stream encoder are input into a multistage bidirectional convolutional gated recurrent unit (MSBC_GRU) feature fusion module, allowing the network to learn the change information in a cross-stage manner. In addition, we use a dual-channel attention module to fuse the features extracted by dual- and single-stream encoders, improving the network’s ability to discriminate changed features. The effectiveness of TSNet is demonstrated with three publicly available CD datasets. The extensive experimental results demonstrate that the proposed method achieves the state-of-the-art CD performance on the above three datasets.

High-resolution remote sensing image (RSI) segmentation is a relatively mature application in various deep learning projects. In this study, aiming at slender objects in road RSIs, BMDANet combines cross-layer information exchange and block multi-dimensional attention (BMDA) module and optimizes road feature extraction by using multi-dimensional information to construct a global attention module. The experimental results based on the Ottawa road dataset show that our algorithm improved the recognition results of the road in RSI, and excelled the existing RSI road segmentation algorithm and reached the state-of-the-art. In addition, based on comparative experiments, the addition of the BMDA module to different algorithms can effectively improve the accuracy of the algorithm. It has proven the effectiveness and embedding of our BMDA module in RSI road segmentation algorithms.

Arbitrary-oriented object detection (AOOD) plays a significant role in image understanding in remote-sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multigrained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to refine the angle prediction with much lower costs than narrowing the granularity of DAE. Furthermore, an Intersection over Union (IoU)-aware FAR-Loss (IFL) is designed to improve the accuracy of angle prediction using an adaptive reweighting mechanism guided by IoU. Extensive experiments are performed on several public remote-sensing datasets, which demonstrate the effectiveness of the proposed MGAR. Moreover, experiments on embedded devices demonstrate that the proposed MGAR is also friendly for lightweight deployments.

Characteristics of radiance spectrum and radiance error distribution after channel center wavelength shift of imaging spectrometers were analyzed in this paper. The results show that shifts of the channel center wavelength can cause shifts of the radiance spectrum, and the shift direction of the two is opposite. The radiance error caused by the channel center wavelength shift is basically distributed near the solar and atmospheric absorption bands. When the center wavelength shifts the same amount towards the positive and negative directions, the absolute value of radiance error is different, which is caused by the asymmetry of spectral absorption region. For 5nm, 10nm and 15nm resolution imaging spectrometers, there is a significant linear relationship between the center wavelength shift and the maximum percent error of radiance. When the center wavelength shift value is less than 10% of the spectral bandwidth, the 10nm imaging spectrometer is most sensitive to the center wavelength shift; when the center wavelength shift is greater than 30%, the 15nm imaging spectrometer is most sensitive to the center wavelength shift.

The land-use map is an important data that can reflect the use and transformation of human land, and can provide valuable reference for land-use planning. For the traditional image classification method, producing a high spatial resolution (HSR), land-use map in large-scale is a big project that requires a lot of human labor, time, and financial expenditure. The rise of the deep learning technique provides a new solution to the problems above. This paper proposes a fast and precise method that can achieve large-scale land-use classification based on deep convolutional neural network (DCNN). In this paper, we optimize the data tiling method and the structure of DCNN for the multi-channel data and the splicing edge effect, which are unique to remote sensing deep learning, and improve the accuracy of land-use classification. We apply our improved methods in the Guangdong Province of China using GF-1 images, and achieve the land-use classification accuracy of 81.52%. It takes only 13 hours to complete the work, which will take several months for human labor.

Large-scale Landsat image classification is essential for the production of land cover maps. The rise of convolutional neural networks (CNNs) provides a new idea for the implementation of Landsat image classification. However, pixels in Landsat images have higher uncertainty compared with high-resolution images due to its 30-m spatial resolution. In addition, the current deep learning methods tend to lose detailed information such as boundaries along with the stacking of convolutional and pooling layers. To solve these problems, we propose a new method called entropy and MRF model (EMM)-CNN based on Pyramid Scene Parsing Network. The EMM-CNN uses entropy to decrease the uncertainty of pixels. Then, the Markov random filed (MRF) model is employed to construct the connections between neighboring pixels and defined a prior distribution to prevent the cross entropy from sacrificing detailed information for the overall accuracy. Finally, transfer learning based on the pretrained ImageNet is introduced to overcome the shortage of training samples and boost the speed of the training process. Experimental results demonstrate that the proposed EMM-CNN is able to obtain classification results with fine structure by decreasing the uncertainty and retaining detailed information of the detected image.

Since the 1960s, remote sensing (as an innovative, comprehensive, and interdisciplinary academic area) has been adopted in a wide range of disciplines related to Earth observation, including hydrology, ecology, oceanography, glaciology, geology, military, intelligence, business, economy, and planning [1]-[3]. The constant development of the remote sensing image acquisition technology now allows for the collection of a wide variety of images with different characteristics and resolutions, obtained by remote sensing instruments mounted on spacecraft or aircraft platforms. These images record some type of signal or energy measured from the Earth's surface, which depends on the type of sensor used.

Remote sensing has evolved into a multidisciplinary field involving many different areas such as sensor technology, computing, and advanced applications. Information extraction now plays a fundamental role in the exploitation of the massive amount of data collected by earth observation instruments. In this Point of View, the authors analyze the evolution of this field, identifying three main phases in its development. The first period, which was marked by advances in digital signal processing, led to a significant development of statistical processing methods. The second phase was based on advances in physical models and brought an era of quantitative remote sensing which lasted until the first decade of this century. In the third and current period, information extraction techniques are gradually adopting advanced artificial intelligence models in an effort to cope with the tremendous increase in data volume. This article describes some of these recent advances and addresses challenges caused by the 4Vs (volume, velocity, variety, and veracity) of big data. Finally, the authors offer insight into future directions in this multidisciplinary field.

The imaging Fourier transform spectrometer (IFTS) carried on HJ-1A is the first spaceborne hyperspectral earth observation sensor in China. Shifts in the spectral channel center wavelengths of IFTS may occur after launch due to vibrations, and to changes in instrument temperature and pressure. In this paper, we described an improved on-orbit spectral calibration method, which is based on spectrum angle matching between the spectrometer-measured radiance spectrum and the modeled radiance spectrum at atmospheric absorption bands. Compared with the laboratory calibration result of IFTS, spectral shifts ranged from -3.18 to -3.4 nm were derived depending on cross-track spectral position at 760-nm oxygen bands, and from -3.53 to -3.79 nm at 820-nm water vapor bands. With the updated spectral calibration coefficient, the reflectance of vegetation and desert in our study site were reconstructed by applying a further atmospheric correction, and the strong spikes around the atmospheric absorption bands were almost obviously suppressed.

HJ-1A is one member of the Environment and Disaster Monitoring Microsatellite Constellation, which carries a HyperSpectral Imager (HSI) with the spectral resolution about 5nm from 450nm to 950nm. Retrieving columnar water vapor content is essential to the quantitative applications of hypespectral imagery. The ideal best band (940nm) for water vapor retrieval has lower signal-to-noise(S/N) value. In this paper, we choose another weak water absorption band (820nm) to retrieval the columnar water vapor content. Two common water vapor retrieval algorithms, called the Continuum Interpolated Band Ratio (CIBR) and the Atmospheric Precorrected Differential Absorption (APDA), are implemented and compared with each other. Simulation results show that these two algorithms have less difference in the accuracy of water vapor retrieval because the weak water vapor absorption effect in 820nm The water vapor contents derived from HJ-1A HSI by CIBR algorithm are compared with MODIS products and a systematic error may exist in the radiometric calibration of HSI.

HJ-1A and HJ-1B, the first micro-satellite constellation for Environment and Disaster Monitoring of China, was successfully launched in China on September 6, 2008. The same multispectral imagers named HJ-1/CCD with four bands (R, G, B, Nir) and large swath are installed on both HJ-1A and HJ-1B. The HJ-1/CCD is the main sensor of the constellation. In this paper, data sets includes six image pairs of HJ-1A/CCD and Terra/Modis and field reflectance spectra of Dunhuang Calibration Site are acquired. Cross calibration is taken to get the calibration results of HJ-1A/CCD relative to Terra/Modis. Based on the calibration results, the radiometric performance of HJ-1A/CCD is evaluated. Taking into that the good radiometric performance of Terra/Modis, it is obvious that HJ-1A/CCD has a radiometric attenuation during its second half of the first year. The max attenuation occurred on the near infrared band is about 18%. The min attenuation occurred on the blue band is about 8%. There maybe some inevitable errors about the data sets and produced during the processing. More images should be acquired and more attention should be paid to get the true radiometric attenuation.

To fulfill the objectives of Beijing-1 micro-satellite on-orbit calibration experiment, two in-flight calibration experiments were carried out on September 3 and 13, 2008 at Dunhuang Calibration Site. Two simultaneous datasets including Beijing-1 data and the in-situ surface reflectance and atmospheric measurements, were acquired at the calibration area. During the period of the calibration experiments, some dark sea images were captured to calculate the dark current of Biejing-1. With the reflectance of calibration targets, the atmospheric optical depths, and the water vapor constraints, the 6S radiative transfer code was used to calculate the equivalent radiance of Beijing-1 to the top of the atmosphere (TOA). The reflectance calibration algorithm was taken to calculate the absolute radiometric calibration coefficients of Beijing-1 multi-spectral cameras. The calibration results of Sep. 3 and that of Sep. 13 are very similar. The biggest relative error between the two results is only 3.77%. This proves the calibration results are reasonable.

Lunar minerals mapping is one of basic aims of China's Lunar Exploration Program. The goal of this paper was to use multiple mineral mapping techniques including classification and spectral matching for lunar surface minerals mapping and choose the effective methods based on the image data which was simulated by 76 lunar samples spectra supplied by LSCC. The results indicated that Mahalanobis Distance and support vector machine performs best of the supervised classification methods. SAM is more effective than SID of the spectral matching methods. The classification capability was different for the different size samples of the same materials. The samples with obvious diagnosed spectral characteristic can be identified effectively. Those without diagnosed spectral characteristic are sensitive to the mapping method. Besides the mapping methods, there are other factors which may affect the mapping results, such as the lunar soil component, the lunar soil maturity, the particle size and the data preprocessing procedure.

Many image fusion techniques have been proposed so as to achieve optimal resolution in the spatial and spectral domains. The Beijing-1 Micro-Satellite images have their own unique characteristics. The resolution ratio of its multi-spectral image to panchromatic image exceeds 4:1. It is often a challenge to pan-sharpen images with such a large spatial resolution ratio. In this paper, we develop an improved fusion method based on IHS, wavelet transform and regional features to merge the panchromatic and multi-spectral images of Beijing-1 Micro-Satellite. In addition, a comparative analysis from both visual effect and quantization parameters is carried out against other existing strategies. The results show that our proposed method can achieve better performance in combining and preserving spectral-spatial information for the Beijing-1 Micro-Satellite test images.

A Micro-satellite Constellation for Environment and Disaster Monitoring was successfully launched in China on September 6, 2008, which includes two small satellites, Satellite-A (HJ-1A) and Satellite-B (HJ-1B). The interferometric imaging spectrometer (IFIS) installed on HJ-1A is the first hyperspectral earth observation sensor in China. To assess the data quality of IFIS, a calibration experiment was carried out at the Dunhuang Calibration Site on October 20, 2008. With the simultaneous measurements acquired from the Dunhuang calibration field, the 6s radiative transfer code was used to retrieve the ground surface reflectance. By comparing the in-situ reflectance and 6S reflectance of the Dunhuang calibration target, the radiometric and spectral performance of the IFIS was evaluated. From the homogeneous image of the calibration target, the Signal-to-Noise Ratio (SNR) of IFIS data was estimated based on the high correlation between bands. This noise estimation results was used to estimate noise covariance matrix needed for hyperspectral data dimension reduction, such as Maximum Noise Fractions (MNF). The assessment results indicated that the IFIS has good performance and will be promising in the applications of environment and disaster monitoring.

An extended Bragg scattering model, for fully polarimetric SAR data, is here proposed for describing sea surface scattering. Moreover, the model is considered to examine the scattering contributions from sea surface and detected dark areas due to the presence of anthropogenic and biogenic slicks. Experiments are conducted on fully polarimetric C- and L-band SAR data.

As a key parameter representing the outgoing solar flux fractions reflected by earth surface, the albedo of land surface has been strongly altered by the change of land covers, especially the increase of the impervious surface caused by the urbanization. This paper discussed and demonstrated the albedo changed with the impervious increases around Beijing-Tianjin urban region, where land cover type has been strongly changed since the 1980s, especially a large scale of buildings and roads increased in Beijing 2008 Olympic Games. We extracted the variation information of albedo from MODIS data as well as the impervious changes by using two scenes Landsat Thematic Mapper images. The relationship between the change pattern of albedo and the impervious surface was discussed and especially the regions within the five rim of Beijing urban, and the surrounding areas along two Jing-Jin highways were paid more attention to. It was found that the variation of the near infrared albedo albedo in 2008 shows an obvious change with the increase of impervious surface, while the change is not apparent in the visible band.