Bibliographic record
Abstract
Texture is an important feature which can be used in the interpretation of remotely sensed imagery. This paper presents exam- ples of the application of a new approach to the textural filtering and enhancing of digital images. Satisfactory results are obtained in pro- cessing images from both natural textures and airborne SAR scenes. Textural filtering of SAR images can be useful in improving the dis- crimination between lithologic units with different surface-roughness characteristics. One application example is discussed in which textural features show different discrimination performances before and after textural filtering. I. INTRODUCTION A digital image is usually characterized by two main aspects: Tone and texture. The image tone consists of background grey- level variations of the pixels throughout the entire image. The im- age texture represents the intrinsic spatial variability of neighbor- ing pixel values for each pixel within the image. It follows that methods of image analysis can be broadly divided into two cate- gories: The spectral one and the textural one. In spectral analysis the interest is in studying broad variations of the grey levels of the pixels (image tone) in mono-, or multispectral bands. The aim of textural analysis is to characterize spatially the grey-level relation- ships between the pixels of a neighborhood. Tone and texture are usually not independent in an image, so that when processing it we observe that one influences the other. The image tone characteris- tics can be separated from the texture, and these two features can be processed independently. This situation promises to be very use- ful in enhancing the texture perception on the one hand, and in improving the result of conventional texture analysis on the other. Digital filtering techniques are widely used in image processing and interpretation ( 11-(3). Based on the traditional concept of Fourier analysis, the three basic classical filters are the low-pass, the high-pass, and the band-pass. They are widely used in remote sensing for noise suppression, edge detection, smoothing, and en- hancement. However, these filters are unable to separate the tone and texture because in an image the two features affect each other. A new method of texture analysis has been recently proposed by He and Wang (4), (5), where a texture image can be described by the characteristics of its texture spectrum. The purpose of this pa- per is to design a textural filter in the texture spectrum domain to remove the regional intensity background variation, termed the tex- tural noise. The performance of the new textural filter on several of Brodatz's natural images (8) is discussed. In airborne SAR terms, the new filter has been shown to enhance the textural perception and improve the discrimination between different rock units.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".