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Due to the instantaneous field-of-view (IFOV) of the sensor and diversity of land cover types, some pixels, usually named mixed pixels, contain more than one land cover type. Soft classification can predict the portion of each land cover type in mixed pixels in the absence of spatial distribution. The spatial distribution information in mixed pixels can be solved by super resolution mapping (SRM). Typically, SRM involves two steps: soft class value estimation, which is similar to the image super resolution of image restoration, and land cover allocation. A new SRM approach utilizes a deep image prior (DIP) strategy combined with a super resolution convolutional neural network (SRCNN) to estimate fine resolution fraction images for each land cover type; then, a simple and efficient classifier is used to allocate subpixel land cover types under the constraint of the generated fine fraction images. The proposed approach can use prior information of input images to update network parameters and no longer require training data. Experiments on three different cases demonstrate that the subpixel classification accuracy of the proposed DIP-based SRM approach is significantly better than the three conventional SRM approaches and a transfer learning-based neural network SRM approach. In addition, the DIP-SRM approach performs very robustly about small-area objects within multiple land cover types and significantly reduces soft classification uncertainty. The results of this paper provide an extension for utilizing SRCNN to address SRM issues in hyperspectral images. [ABSTRACT FROM AUTHOR]

The rational function model (RFM) is widely applied to orthorectification of aerial and satellite imagery. This article proposes a newmethod named Ortho-WTLS to solve the RFM in remote-sensing imagery orthorectification. Based on a weighted total least squares (WTLS) estimator, the proposed method allows one to handle coordinates of ground control points (GCPs) that contain errors and are of unequal accuracies. This situation occurs, e.g. if GCPs are automatically selected. In the proposed model, first, the relationship of two linearization methods for an RFM with errors contained in GCPs is investigated and results in a hybrid linearization. Next, based on WTLS, RFM coefficients are estimated with an iterative computation function. Finally, the performance of the Ortho-WTLS method thus obtained is investigated using simulated images and remotely sensed images by collecting GCPs with varying errors. Experimental results show that the Ortho-WTLS method achieves a more robust estimation of model parameters and a higher orthorectification accuracy when compared with standard LS-based RFM estimation. We conclude that the quality of GCPs has a large impact on the accuracy and that an increasing number of low-precision GCPsmay lead to a decrease in orthorectification quality. [ABSTRACT FROM AUTHOR]