Quantifying Information Content in Multispectral Remote-Sensing Images Based on Image Transforms and Geostatistical Modelling

Quantifying information content in remote-sensing images is fundamental for information-theoretic characterization of remote sensing information processes, with the images being usually information sources. Information-theoretic methods, being complementary to conventional statistical methods, enable images and their derivatives to be described and analyzed in terms of information as defined in information theory rather than data per se. However, accurately quantifying images’ information content is nontrivial, as information redundancy due to spectral and spatial dependence needs to be properly handled. There has been little systematic research on this, hampering wide applications of information theory. This paper seeks to fill this important research niche by proposing a strategy for quantifying information content in multispectral images based on information theory, geostatistics, and image transformations, by which interband spectral dependence, intraband spatial dependence, and additive noise inherent to multispectral images are effectively dealt with. Specifically, to handle spectral dependence, independent component analysis (ICA) is performed to transform a multispectral image into one with statistically independent image bands (not spectral bands of the original image). The ICA-transformed image is further normal-transformed to facilitate computation of information content based on entropy formulas for Gaussian distributions. Normal transform facilitates straightforward incorporation of spatial dependence in entropy computation for the aforementioned double-transformed image bands with inter-pixel spatial correlation modeled via variograms. Experiments were undertaken using Landsat ETM+ and TM image subsets featuring different dominant land cover types (i.e., built-up, agricultural, and hilly). The experimental results confirm that the proposed methods provide more objective estimates of information content than otherwise when spectral dependence, spatial dependence, or non-normality is not accommodated properly. The differences in information content between image subsets obtained with ETM+ and TM were found to be about 3.6 bits/pixel, indicating the former’s greater information content. The proposed methods can be adapted for information-theoretic analyses of remote sensing information processes.

Download Full-text

What’s Worth Talking About? Information Theory Reveals How Children Balance Informativeness and Ease of Production

Psychological Science ◽

10.1177/0956797617699848 ◽

2017 ◽

Vol 28 (7) ◽

pp. 954-966 ◽

Cited By ~ 8

Author(s):

Colin Bannard ◽

Marla Rosner ◽

Danielle Matthews

Keyword(s):

Information Theory ◽

Information Content ◽

Low Frequency ◽

Initial Experiment ◽

Information Theoretic ◽

Information Theoretic Measures

Of all the things a person could say in a given situation, what determines what is worth saying? Greenfield’s principle of informativeness states that right from the onset of language, humans selectively comment on whatever they find unexpected. In this article, we quantify this tendency using information-theoretic measures and report on a study in which we tested the counterintuitive prediction that children will produce words that have a low frequency given the context, because these will be most informative. Using corpora of child-directed speech, we identified adjectives that varied in how informative (i.e., unexpected) they were given the noun they modified. In an initial experiment ( N = 31) and in a replication ( N = 13), 3-year-olds heard an experimenter use these adjectives to describe pictures. The children’s task was then to describe the pictures to another person. As the information content of the experimenter’s adjective increased, so did children’s tendency to comment on the feature that adjective had encoded. Furthermore, our analyses suggest that children balance informativeness with a competing drive to ease production.

Download Full-text

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

Remote Sensing ◽

10.3390/rs12020205 ◽

2020 ◽

Vol 12 (2) ◽

pp. 205 ◽

Cited By ~ 7

Author(s):

Moyang Wang ◽

Kun Tan ◽

Xiuping Jia ◽

Xue Wang ◽

Yu Chen

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Change Detection ◽

Decision Model ◽

Multispectral Images ◽

Remote Sensing Images ◽

Convolutional Network ◽

Siamese Network ◽

The Difference ◽

Computational Budget

Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

Download Full-text

Effects of Noise on the Information Content of Remote Sensing Images

Geocarto International ◽

10.1080/10106040308542269 ◽

2003 ◽

Vol 18 (2) ◽

pp. 15-26 ◽

Cited By ~ 7

Author(s):

Ram M. Narayanan ◽

Sudhir K. Ponnappan ◽

Stephen E. Reichenbach

Keyword(s):

Remote Sensing ◽

Information Content ◽

Remote Sensing Images

Download Full-text

Salient region detection in remote sensing images based on color information content

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2015.7326159 ◽

2015 ◽

Author(s):

Libao Zhang ◽

Shuang Wang ◽

Xuewei Li

Keyword(s):

Remote Sensing ◽

Information Content ◽

Color Information ◽

Remote Sensing Images ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection

Download Full-text

Sub-pixel mapping of remote sensing images based on sub-pixel/pixel spatial attraction models with anisotropic spatial dependence model

2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS) ◽

10.1109/eiis.2017.8298743 ◽

2017 ◽

Author(s):

Chunhui Zhao ◽

Huaijuan Yang ◽

Haifeng Zhu ◽

Yiming Yan

Keyword(s):

Remote Sensing ◽

Spatial Dependence ◽

Remote Sensing Images ◽

Pixel Mapping ◽

Spatial Attraction

Download Full-text

Ship Detection in Multispectral Satellite Images Under Complex Environment

Remote Sensing ◽

10.3390/rs12050792 ◽

2020 ◽

Vol 12 (5) ◽

pp. 792 ◽

Cited By ~ 4

Author(s):

Xiaoyang Xie ◽

Bo Li ◽

Xingxing Wei

Keyword(s):

Remote Sensing ◽

Weather Conditions ◽

Detection Methods ◽

Multispectral Images ◽

Complex Environments ◽

Remote Sensing Images ◽

Ship Detection ◽

Additional Information ◽

Multispectral Satellite Images ◽

New Feature

Ship detection in multispectral remote-sensing images is critical in marine surveillance applications. The previously proposed ship-detection methods for multispectral satellite imagery usually work well under ideal conditions. When meeting complex environments such as shadows, mists, or clouds, they fail to detect ships. To solve this problem, we propose a novel spectral-reflectance-based ship-detection method. Research has shown that different materials have unique reflectance curves in the same spectral wavelength range. Based on this observation, we present a new feature using the reflectance gradient across multispectral bands. Moreover, we propose a neural network called lightweight fusion networks (LFNet). This network combines the aforementioned reflectance and the color information of multispectral images to jointly verify the regions with ships. The method utilizes a coarse-to-fine detection framework because of the large-sense-sparse-targets situation in remote-sensing images. In the coarse stage, the proposed reflectance feature vector is used to input the classifier to rule out the regions without ships. In fine detection, the LFNet is used to verify true ships. Compared with some traditional methods that merely depend on appearance features in images, the proposed method takes advantage of employing the reflectance variance in objects between each band as additional information. Extensive experiments have been conducted on multispectral images from four satellites under different weather and environmental conditions to demonstrate the effectiveness and efficiency of the proposed method. The results show that our method can still achieve good performance even under harsh weather conditions.

Download Full-text

Geostatistical modelling of spatial dependence in area-class occurrences for improved object-based classifications of remote-sensing images

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2018.05.003 ◽

2018 ◽

Vol 141 ◽

pp. 219-236 ◽

Cited By ~ 2

Author(s):

Yunwei Tang ◽

Jingxiong Zhang ◽

Linhai Jing ◽

Han Gao

Keyword(s):

Remote Sensing ◽

Spatial Dependence ◽

Remote Sensing Images ◽

Geostatistical Modelling ◽

Object Based

Download Full-text

An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

Remote Sensing ◽

10.3390/rs12030466 ◽

2020 ◽

Vol 12 (3) ◽

pp. 466

Author(s):

Xue Yang ◽

Feng Li ◽

Lei Xin ◽

Xiaotian Lu ◽

Ming Lu ◽

...

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Satellite Images ◽

Super Resolution ◽

Geostationary Orbit ◽

Image Texture ◽

Support Vector ◽

Multispectral Images ◽

Remote Sensing Images ◽

Orbit Satellite

Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.

Download Full-text