To effectively solve the accurate identification of gray ice, melt ponds water, floe, brash ice, and thin ice in the melting state of the Arctic Sea ice during summer, we propose adding a batch normalization layer and adaptive moment estimation optimizer of a U-NET (BAU-NET) method for Arctic Sea ice semantic segmentation in summer from remote sensing satellite optical images. The U-NET network structure is optimized to 18 convolution layers, and a batch normalization layer and a nonlinear activation function rectified linear unit are added behind each convolution layer. Then the hyper-parameters of the network structure are adjusted. The cross-entropy loss function based on SOFTMAX and L2 regularization are used in training the model, and the adaptive moment estimation optimizer is used for iterative training until the error convergence. The experimental results show that the accuracy, precision, recall, and F1 score of sea ice extraction results reaches more than 97.26%. Compared with the DeepLabv3 and U-Net methods, the sea ice prediction time efficiency is improved by 90.99s and 1.57s, respectively, and the accuracy is improved by 6.59% and 8.05%, respectively, which indicate that the sea ice prediction time efficiency and accuracy of the BAU-NET method are significantly improved.
In the remote sensing image processing field, cloud and snow detection for high-resolution sensors and cloud and snow morphology in different latitudes of the world is challenging. A deep learning training model (Softmax) was developed to improve the accuracy of cloud and snow identification from Gaofen-1 and Pakistan Remote Sensing Satellite-1 images. First, more than 1800 scenes remote sensing images in various regions over the world are collected. Next, the texture details and spectral information of the objects are extracted. Finally, the Softmax model is applied to process the features to obtain the final cloud and snow masks. The cloud and snow detection results are evaluated by performing statistical analysis. The overall accuracy for cloud detection reaches 92.64% (kappa coefficient = 0.83) and for snow detection reaches 93.94% (kappa coefficient = 0.8). The algorithm is not only accurate but also computationally efficient. It is of great importance for image processing in ground segment and corresponding applications.
Urban grass is the interference object of vegetable species recognition. Therefore choose an instance of urban grass to
retrieve the spectrum curve of interference vegetation. The spectrum retrieval of vegetation species includes three steps,
1) the Hyperspectral image preprocessing, 2) the high fidelity image fusion, and 3) the purity endmember extraction.
Firstly, the Hyperspectral image is preprocessed including the removal of bad bands, the radiance calibration, and the
FLAASH atmospheric correction. Secondly, the Gram-Schmidt fusion method which has an advantage of spectral high
fidelity was employed to fuse the Hyperspectral image and the high spatial panchromatic image. Thirdly, the grass
reference vectors was applied in masking the fusion image and then the minimum noise fraction was used to forward and
inverse transform the masking image. The pixel purity index of image was calculated after de-noising and then the
threshold range was determined to obtain the region of interest that has high purity. The principal component analysis
was adopted to forward transform the visible, near infrared, shortwave infrared channels respectively and then the first
and second bands of each channel were selected. The optimum index factor was used to acquire the eigenvalues of
optimum bands combination and then the N-dimensional visualization was applied in extracting study area endmember
of grass species. Finally the spectrum curve of urban grass was retrieved from the average endmember spectral of
original fusion image.
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