Wenjuan Zhang

A New Operational Method for Estimating Noise in Hyperspectral Images

A new method for estimating noise in hyperspectral images is described in this letter. Our method is based on the general internal regularity of Earth objects and the strong spectral correlation of hyperspectral images. It can be used to automatically estimate noise for both radiance and reflectance images. Unlike other methods discussed in this letter, our method is more reliable and adaptable, which we demonstrate using simulated images with different scene contents. Finally, we successfully applied this new method in estimating noise for pushbroom hyperspectral imager (PHI) data.

A neighbourhood-constrained k-means approach to classify very high spatial resolution hyperspectral imagery

In classifying very high spatial resolution (VHR) hyperspectral imagery, intra-class variation often adversely affects classification accuracy, mainly due to a low signal-to-noise ratio (SNR) and high spatial heterogeneity. To address this problem, this article develops a neighbourhood-constrained k-means (NC-k-means) algorithm by incorporating the pure neighbourhood index into the traditional k-means algorithm. The performance of the NC-k-means algorithm was assessed through a series of simulated images and a real hyperspectral image. The results indicate that the classification accuracy of NC-k-means algorithm is consistently better than that of the traditional k-means algorithm, in particular for the images with significant spatial autocorrelations among neighbouring pixels.

Analysis of the Proportion of Surface Reflected Radiance in Mid-Infrared Absorption Bands

Image simulation of remote sensing systems is important for the development of new instruments and validations of data processing algorithms. In image simulation process, surface scene simulation is a fundamental issue and usually has the first priority. For two mid-infrared absorption bands near 2.7 μm and 4.3 μm, although there are a lot of applications in remote sensing field, relevant research on surface scene simulation is very limited. In these two mid-infrared ranges, surface radiance is a combination of reflected and emitted radiance. However, the radiance is generally reduced because of strong absorption by atmosphere. Therefore, analysis of surface reflected radiance is essential for simulation work. In this paper, we use a radiative transfer model MODTRAN to simulate proportions of surface reflected radiance for common ground materials under various observation conditions. The obtained results show that proportions of studied materials are 0.8%-99.8% in the band of 2.63-2.83 μm and 1.1%-94.8% in the band of 4.2-4.5 μm. The proportions of surface reflected radiance in both absorption bands are affected by surface reflectivity. In addition, in the band of 2.7 μm the proportion of surface reflected radiance is sensitive to solar geometry, water vapor content and surface temperature, whereas it is insensitive in the band of 4.3 μm. Based on these results, we conduct that both reflection and emission are important for surface scene simulations in the 2.7 μm and 4.3 μm absorption bands.

Top-of-Atmosphere Image Simulation in the 4.3-

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.

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Multi-mode Airborne Digital Camera System (MADC) was developed by Institute of Remote Sensing Applications and Shanghai Institute of Technical Physics in 2006. Several finished aerial experiments have already demonstrated that the system has good performance for aerial photography. But the image smear which leads by position change and forward motion of aircraft has adverse effect on the images quality. So finding an effective way to realize image smear compensation is a key technique to improve and develop unceasingly for MADC system. We have designed the external image smear compensation module and written the special image processing soft to compensate the image smear. Some experiments and simulations in laboratory or on the ground have shown that the two ways for image smear compensation are both useful for getting better aerial remote sensing images by MADC system. Aerial experiments will be implemented to verify these methods further.

Mid-infrared Absorption Bands

Land surface temperature (LST) and land surface emissivity (LSE) are two critical parameters in the physics of land surface processes at different scales. In this paper, an improved linear spectral emissivity constraint method is proposed to make the retrieved emissivity spectra be continuous at the adjacent segments. To evaluate the proposed method, simulation data covering various land surface and atmospheric conditions are generated. The results show the magnitudes of the RMSE of LST is 0.046 K and those of LSEs is 0.002 when there is a white noise of NEΔT about 0.1 K. Taking the advantages of holding continuity and noise-immune into account, the proposed method is promising.

Realization of image smear compensation for multi-mode airborne digital camera system

The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.

Improvement of linear spectral emissivity constraint method for temperature and emissivity separation of hyperspectral thermal infrared data

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.

An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images

The requirements for sensor parameters of different application are different. This study evaluated the influences of signal-to-noise ratio (SNR), spectral resolution (SR) and ground sample distance (GSD) of hyperspectral remote sensing data for the mineral mapping and revealed the requirements of these sensor parameters. A SNR centered parameters optimization method was proposed to the optimization of sensors for the improvement of the mineral extraction performance. The simulated image dataset based on the Airborne Visible/Infrared Imaging Spectrometer (AVTRIS) data was used for the evaluation of the influences of sensor parameters and the performance of optimization. Spectral angle mapper (SAM) and spectral feature fitting (SFF) were applied to the simulated hyperspectral data. The results of the study showed that the sensor optimization improved the performance of the mineral extraction. The requirements and optimization of sensor parameters will contribute to the proper trade-offs design of programmable imaging spectrometers for specific scientific application in the future space systems.

A reflectance image simulation method for atmospheric absorption bands centered at 2.7 micron

The 2.7 micron absorption bands are applied in military target detection. Since pre-launch image simulation is necessary for development and optimization of a sensor, image simulation should be conducted for the 2.7 micron absorption bands. In this paper, an emissivity image simulation method is proposed, and the accuracy of proposed emissivity generation models is analyzed on 16 kinds of spectra from JHU library. According to experiment results, vegetation and water have small relative errors, which are −2.3% and −2.86% at maximum. Soil and manmade materials have relatively large errors that reach to −7.6% and 9.54%, respectively.