The proposed algorithm is based on the linear image features analysis (we call those features "stripes"). LESSA automatically detects stripes and determines their direction (8 directions). Features detected as stripes must be straight and long enough. In a grey tone image homogeneous region edges (and/or grey tone lines) are detected as "stripes".

In DTM ridges and valleys axis are detected. (Automatic drainage network detection and analysis is also possible.) It’s important to notice that all stripes detected in DTM resemble only relief features (not vegetation or artificial features).

There are two different methods of the stripes analysis that provide quite different results - statistical analysis and the long linear lineaments detection. Statistical analysis is the most developed and objective method. Total number of the stripe pixels of each orientation is calculated in the given neighborhood and displayed by so called rose-diagram (or simple, rose). This data is calculated either in the user defined neighborhood (blocks) or in the picture point neighborhood (the circular scanning window). As a matter of fact, rose-diagrams represent orientation image (texture) properties.

The scanning window calculations form large matrixes (we call them "digital fields") of each orientation density and of roses shape descriptors such as elongation, crusiformance, neighborhood rose difference and others. One can analyze digital fields visually - they are displayed as pseudocolored images - looking for sharp gradient and anomalous zones. Digital fields are used also for decision making in GIS (examples).

The scanning window results are displayed also by "drawing fields" such as rose-diagrams, vectors of the rose-diagrams elongation, vectors of the rose-diagrams maximal direction and so on. Main orientation texture features are presented in LESSA by so-called orientation net ("OR-net") - lines along roses elongation (blue lines) and across elongation (red lines). Several "drawing fields" could be displayed simultaneously and along with any "digital field" or other image.

Several LESSA tools help user to achieve meaningful, "readable" results. For example, if there is one major stripes direction in the image that masks all local orientation features, one can use so called "global normalization" to extract these local features.

Regarding the long lineaments detection, the stripes detected at the first step are combined into the straight lines - lineaments. There could be gaps between the stripes but the lineaments must be straight and long enough. Validity measure shows how good lineament is filled with stripes. User can display lineaments of the given direction and "validity", select them and combine into a lineament scheme. User chooses lineaments for the scheme himself, subjectively, but he chooses among lineaments that are detected by formal criteria, objectively. User can find lineaments that he did not detect in the image himself; he can also examine lineaments structure (stripes that form it). User can change calculation accuracy to find optimal time-accuracy relation.

It’s very important to seek lineaments of the proper length (not too long), to take into consideration lineaments and stripes length relation. Some times it’s better to look for shorter lineaments that do not cross the whole image. It’s easy to see that shorter lineaments (3 and 6 times in the given example) can form somewhat different schemes.