Most of the Antarctic continent is covered with ice and snow. However, it’s hard to distinguish clouds from ice and snow
in remote sensing images because they both have similar characteristics in visible reflectances and infrared brightness
temperatures. Thus there exist great difficulties in determining the precise locations and distribution of clouds in remote
sensing images. To solve this problem, a new method is proposed to identify clouds for Landsat imagery over the
Antarctic region. Top of atmosphere reflectance and brightness temperature of Landsat imagery are used as inputs.
Several spectral tests combining with morphological operations are employed to highlight clouds, especially thin clouds.
The results show that the new method can not only greatly eliminate the effects of snow and ice, but also extract thin
clouds effectively, and thus improve cloud detection accuracy over the Antarctic region.
Texture features are widely used in image retrieval literature. However, conventional texture features are extracted from grayscale images without taking color information into consideration. We present two improved texture descriptors, named color Gabor wavelet texture (CGWT) and color Gabor opponent texture (CGOT), respectively, for the purpose of remote sensing image retrieval. The former consists of unichrome features computed from color channels independently and opponent features computed across different color channels at different scales, while the latter consists of Gabor texture features and opponent features mentioned above. The two representations incorporate discriminative information among color bands, thus describing well the remote sensing images that have multiple objects. Experimental results demonstrate that CGWT yields better performance compared to other state-of-the-art texture features, and CGOT not only improves the retrieval results of some image classes that have unsatisfactory performance using CGWT representation, but also increases the average precision of all queried images further. In addition, a similarity measure function for proposed representation CGOT has been defined to give a convincing evaluation.