iTSensing Documentation Portal

Central index for current Remote Sensing feature guides, scientific modules, satellite-data specifications, export references and extended documentation. Search is handled from a compact prebuilt metadata index, so the browser does not scan every HTML document while you type.

Search Index Mode54 documents15 categories Static / offline friendlyPre-indexed search
Navigation model: this front page controls the documentation collection. Each result opens one standalone HTML guide, and the imported long-form references have a floating “Documentation Index” button to return here.

Remote Sensing Core

15 documents
Spectral PreprocessingPrepare multispectral or hyperspectral imagery before spectral analysis. The feature can apply radiometric scaling, optional scene-based atmospheric correction, and band selection while preserving the geospatial raster structure.Hydro PreprocessingPrepare optical imagery for aquatic analysis by generating a water mask, reducing sunglint, and optionally deriving depth-invariant spectral information for shallow-water studies.Raster Spectral & Texture AnalysisCalculate spectral indices and local texture measures from raster bands. Spectral methods combine physically meaningful bands; GLCM methods quantify spatial texture from local gray-level co-occurrence patterns.Edge DetectionDetect sharp spatial transitions such as object boundaries, roads, field edges, structural lines, or coastal boundaries from a selected raster band.Shoreline ExtractionExtract a landwater boundary from optical imagery using a water index, thresholding, mask cleanup, and optional edge refinement.Layer StackingCombine selected bands from one or more rasters into a single aligned multiband raster. It can also reorder or subset bands from one raster.Raster ModellerBuild a raster prediction model from training points with a numeric target field, then predict that continuous variable across the predictor raster.Supervised ClassificationCreate a thematic class raster from labelled training samples. Spectral classifiers learn pixel-feature relationships; CNN/U-Net use spatial neighborhoods or semantic segmentation context.Unsupervised ClassificationAutomatically groups pixels with similar multiband values without labelled training data. The result is a cluster map that must be interpreted before it becomes a semantic land-cover map.OBIA ClassificationObject-Based Image Analysis (OBIA) first segments neighboring pixels into image objects, calculates object attributes, then classifies those objects. This reduces the purely pixel-by-pixel view and can use shape, texture, and context.Image Matching DetectionDetect repeated visually similar objects from user-selected example points. The application builds one or more templates, searches for similar patterns over selected bands and scales, and suppresses duplicate detections.PansharpeningFuse the high spatial detail of a panchromatic image with the spectral content of a lower-resolution multispectral image.Image FusionFuse low-resolution multispectral information with a high-resolution raster using component-based or learned self-supervised fusion methods.ROI AnalysisExtract and analyze raster values inside Regions of Interest (ROI). It supports descriptive statistics, class separability, spectral plots, n-D exploration, scatter plots, histograms, and correlation analysis.Raster Change DetectionCompare two aligned rasters from different dates and produce a continuous or classified change map.

GeoAI

3 documents

Accuracy & Validation

1 documents

SAR / InSAR

3 documents

Shared Raster Tools

8 documents

Accuracy & Rectification

3 documents

Terrain / Elevation

6 documents

Export & Data Delivery

2 documents

Raster Analysis

3 documents

Hydrology

5 documents

Hyperspectral

1 documents

Ocean & Atmosphere

1 documents

Data Catalog & Sensors

1 documents

Extended User Guides

1 documents

Technical Reference

1 documents